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Machine Learning and the Physical Sciences, NeurIPS 2025
All times are local, i.e. Pacific Standard Time
(PST). See also the official NeurIPS workshop schedule.
Panel Discussion: How should we translate ML advances
between academia & industry?
Exploring the interplay of academia & industry using weather and climate research as a case study.
Panel Discussion: What could the "AlphaFold moment" be
for fundamental physics?
A conversation on paradigm-shifting applications across ML and particle/astro/accelerator physics.
-->
Machine Learning and the Physical Sciences
Workshop at the 39th conference on Neural Information Processing Systems (NeurIPS)
Saturday, December 6, 2025
About
Since its inception in 2017, the Machine Learning and the Physical Sciences (ML4PS) workshop has served as a unique gathering space for the growing community spearheading cross-cutting research topics at the intersection of machine learning (ML) and the physical sciences (PS). This includes the applications of ML to problems in the physical sciences (ML for PS) as well as developments in ML motivated by physical insights (PS for ML). The physical sciences are defined inclusively, including but not limited to physics, astronomy, cosmology, chemistry, biophysics, materials science, and Earth science.
The physical sciences pose challenging, high-profile questions that invite innovation and opportunities for cross-pollination between ML researchers and physical scientists. Recent years have seen a tremendous increase in cases where ML models are used for scientific inference and discovery (e.g., geometric deep learning, simulation-based inference), and simultaneously tools and insights from the physical sciences have been used to develop efficient ML models (e.g., diffusion models and physics-informed neural networks). The communities coalescing at this workshop are encouraged to pursue fresh, synergistic solutions to "big science" questions that can spark new approaches in ML.
This year's programming explores the evolving interplay between academia and industry in basic research. Invited talks and panel discussions emphasize the myriad foundational and translational connections between these domains. Furthermore, we ask: how can our community help sustain open, curiosity-driven research in physics and related sciences when traditional funding models are under strain? What role can industry play in supporting fundamental science, and how can the insights emerging from basic research in the physical sciences and ML help catalyze wider innovations in industry and beyond?
NeurIPS 2025
The Machine Learning and the Physical
Sciences 2025 workshop will be held on December 6, 2025 at the San Diego Convention
Center
in San Diego, Californa (USA) as a part of the 39th annual
conference on
Neural Information Processing Systems (NeurIPS). The workshop is planned to take
place in a hybrid format inclusive of virtual participation.
Schedule
| 8:15am - 8:30am | Opening remarks |
| 8:30am - 9:15am |
Invited talk: Michael Albergo |
| 9:15am - 10:00am |
Invited talk: Tess Smidt |
| 10:00am - 10:30am | Coffee break |
| 10:30am - 10:45am | Paper spotlight: Ming-Shau Liu "Continuous Representations of Baryonic Feedback for Robust Inference from Multiple Simulation Suites" |
| 10:45am - 11:00am | Paper spotlight: Kshitij Duraphe "The Platonic Universe: Do Foundation Models See the Same Sky?" |
| 11:00am - 12:00pm | Poster session #1 |
| 12:00pm - 1:00pm | Lunch break |
| 1:00pm - 2:00pm | Panel Discussion: How should we translate ML advances between
academia & industry? Exploring the interplay of academia & industry using weather and climate research as a case study. Panelists: Anima Anandkumar, Peter Harrington, and Laure Zanna |
| 2:00pm - 2:15pm | Paper spotlight: Zichun Hao "RINO: Renormalization Group Invariance with No Labels " |
| 2:15pm - 2:30pm | Paper spotlight: Malachi Schram "Continual Learning for Particle Accelerators " |
| 2:30pm - 3:30pm | Poster session #2 |
| 3:30pm - 4:00pm | Coffee break |
| 4:00pm - 5:00pm | Panel Discussion: What could the "AlphaFold moment" be for
fundamental physics? A conversation on paradigm-shifting applications across ML and particle/astro/accelerator physics. Panelists: Auralee Edelen, Shirley Ho, Jascha Sohl-Dickstein, and Daniel Whiteson |
Job Postings
Are you hiring? Send your job ad to ml4ps@googlegroups.com and we will add it to our spreadsheet:Plenary Speakers
-
Michael AlbergoHarvard, IAIFI
-
Tess SmidtMIT
Panelists
Exploring the interplay of academia & industry using weather and climate research as a case study.
-
Anima AnandkumarCaltech
-
Peter HarringtonNVIDIA
-
Laure ZannaNYU
A conversation on paradigm-shifting applications across ML and particle/astro/accelerator physics.
-
Auralee EdelenSLAC, Stanford
-
Shirley HoNYU, Flatiron
-
Jascha Sohl-DicksteinAnthropic
-
Daniel WhitesonUC Irvine
Papers
Accepted papers are listed below.
| 2 | DiffICF : Diffusion-Driven Inverse Modeling for Laser Pulse Design in
Inertial Confinement Fusion (MLPS)
[paper]
[poster] Ricardo Luna Gutierrez, Vineet Gundecha, Rahman Ejaz, Varchas Gopalaswamy, Riccardo Betti, Sahand Ghorbanpour, Aarne Lees, Soumyendu Sarkar |
| 5 | Tensorization of neural networks for improved privacy and
interpretability
[paper]
[poster] José Ramón Pareja Monturiol, Alejandro Pozas-Kerstjens, David Perez-Garcia |
| 6 | Physics-constrained Plane Wave Decomposition Network: Solving the
Helmholtz Equation in Airborne Acoustics
[paper]
[poster] James Hipperson, Trevor J. Cox, Jonathan Andrew Hargreaves |
| 7 | Embedding conservation structure in neural fields for reduced state
dynamics modeling from sparse and noisy measurements
[paper]
[poster] Aviral Prakash, Ben S. Southworth, Marc Louis Klasky |
| 8 | Latent Target Score Matching, with an application to Simulation-Based
Inference
[paper]
[poster] Joohwan Ko, Tomas Geffner |
| 9 | Adaptive Online Emulation for Accelerating Complex Physical
Simulations
[paper]
[poster] Tara Tahseen, Nikolaos Nikolaou, Luís F. Simões, Kai Hou Yip, Joao Manuel Mendonca, Ingo P. Waldmann |
| 12 | OptICF: Sample-Efficient Optimization of Implosion Outcomes in Inertial
Confinement Fusion
[paper]
[poster] Ricardo Luna Gutierrez, Vineet Gundecha, Rahman Ejaz, Varchas Gopalaswamy, Riccardo Betti, Sahand Ghorbanpour, Aarne Lees, Soumyendu Sarkar |
| 13 | Deceptron: Learned Local Inverses for Fast and Stable Physics
Inversion
[paper]
[poster] Aaditya L. Kachhadiya |
| 14 | Data-driven particle dynamics: Structure-preserving coarse-graining for
emergent behavior in non-equilibrium systems
[paper]
[poster] Quercus Hernández, Max Win, Thomas C. O'Connor, Paulo Arratia, Nathaniel Trask |
| 15 | Scaling Laws and Pathologies of Single-Layer PINNs: Network Width and
PDE Nonlinearity
[paper]
[poster] Faris Chaudhry |
| 16 | Real-Time Neuromorphic Spectrum Intelligence Simulator
[paper]
[poster] Navaneetha Krishnan K |
| 17 | Physics-Informed Neural Controlled Differential Equations for Long
Horizon Multi-Agent Motion Forecasting
[paper]
[poster] Shounak Sural, Charles Kekeh, Wenliang Liu, Federico Pecora, Mouhacine Benosman |
| 19 | Geometric Operator Learning with Optimal Transport
[paper]
[poster] Xinyi Li, Zongyi Li, Nikola Borislavov Kovachki, Anima Anandkumar |
| 20 | Test-time Scaling Techniques in Theoretical Physics - A Comparison of
Methods on the TPBench Dataset
[paper]
[poster] Zhiqi Gao, Tianyi Li, Yurii Kvasiuk, Sai Chaitanya Tadepalli, Daniel J. H. Chung, Maja Rudolph, Frederic Sala, Moritz Münchmeyer |
| 21 | Spacier: A Dataset for Modeling Electrostatic Poisson–Boltzmann Atomic
Solvation Potentials
[paper]
[poster] Yongxian Wu, Ray Luo |
| 22 | Meta-Learning Fourier Neural Operators for Hessian Inversion and
Enhanced Variational Data Assimilation
[paper]
[poster] Hamidreza Moazzami, Asma Jamali, Nicholas Kevlahan, Rodrigo Vargas-Hernandez |
| 23 | Principled Operator Learning in Ocean Dynamics: The Role of Temporal
Structure
[paper]
[poster] Vahidreza Jahanmard, Ali Ramezani-Kebrya, Robinson Hordoir |
| 24 | A Neural Universal Differential Equation (UDE) Approach for Modeling and
Forecasting NIFTY 50 Index (Indian Stock Market Index) Prices and
Drifts
[paper]
[poster] Ishaan Prasad Kulkarni |
| 25 | PhysiX: A Foundation Model for Physics Simulations
[paper]
[poster] Tung Nguyen, Arsh Koneru, Shufan Li, Aditya Grover |
| 27 | Smooth and Sparse Latent Dynamics in Operator Learning with Jerk
Regularization
[paper]
[poster] Xiaoyu Xie, Saviz Mowlavi, Mouhacine Benosman |
| 28 | SeasonCast: A Masked Latent Diffusion Model for Skillful
Subseasonal-to-Seasonal Prediction
[paper]
[poster] Tung Nguyen, Tuan Pham, Troy Arcomano, Rao Kotamarthi, Ian Foster, Sandeep Madireddy, Aditya Grover |
| 32 | Radio Astronomy in the Era of Vision-Language Models: Prompt Sensitivity
and Adaptation
[paper]
[poster] Mariia Drozdova, Erica Lastufka, Vitaliy Kinakh, Taras Holotyak, Daniel Schaerer, Slava Voloshynovskiy |
| 33 | Generative Neural Networks for Kerr Combs
[paper]
[poster] Janet Zhong, Eran Lustig, Shiye Su, Louise Schul, Jamison Sloan, Congyue Deng, Jelena Vuckovic, Shanhui Fan |
| 34 | Universal Spectral Tokenization via Self-Supervised Panchromatic
Representation Learning
[paper]
[poster] Jeff Shen, Francois Lanusse, Liam Holden Parker, Ollie Liu, Tom Hehir, Leopoldo Sarra, Lucas Thibaut Meyer, Micah Bowles, Sebastian Wagner-Carena, Helen Qu, Siavash Golkar, Alberto Bietti, Hatim Bourfoune, Pierre Cornette, Keiya Hirashima, Geraud Krawezik, Ruben Ohana, Nicholas Lourie, Michael McCabe, Rudy Morel, Payel Mukhopadhyay, Mariel Pettee, Kyunghyun Cho, Miles Cranmer, Shirley Ho |
| 35 | FlowTIE: Flow-based Transport of Intensity Equation for Phase Gradient
Estimation from 4D-STEM Data
[paper]
[poster] Arya Bangun, Maximilian Töllner, Xuan Zhao, Christian Kuebel, Hanno Scharr |
| 37 | Differentiable Interference Modeling for Cost-Effective Growth
Estimation of Thin Films
[paper]
[poster] Leonard Storcks, Gunnar Ehlers, Robin Janssen, Konrad Storcks, Tayebeh Ameri, Tobias Buck |
| 38 | Direct Molecular Polarizability Prediction with $\boldsymbol{SO(3)}$
Equivariant Local Frame GNNs
[paper]
[poster] Jean Philip Filling, Michael Wand, Denis Andrienko, Felix Post |
| 39 | Physics-Informed Inverse Design of Optical Coatings using a
Differentiable Transfer Matrix Method
[paper]
[poster] Utsa Chattopadhyay, Florian Carstens, Morten Steinecke, Andreas Wienke, Ingmar Hartl, Nihat Ay, Christoph M Heyl, Henrik Tünnermann |
| 40 | Neural Field Turing Machine: A Differentiable Spatial Computer
[paper]
[poster] Akash Malhotra, Nacera seghouani |
| 41 | A Bio-Inspired Hierarchical Temporal Defense for Securing Spiking Neural
Networks Against Physical and Adversarial Perturbations
[paper]
[poster]
[video] Sylvester Kaczmarek |
| 44 | Blind Strong Gravitational Lensing Inversion: Joint Inference of Source
and Lens Mass with Score-Based Models
[paper]
[poster]
Gabriel Missael Barco, Ronan Legin, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur |
| 45 | Sequential decoder training for improved latent space dynamics
identification
[paper]
[poster] William Anderson, Youngsoo Choi, Seung Whan Chung |
| 47 | Rollout-LaSDI: Enhancing the long-term accuracy of Latent Space
Dynamics.
[paper]
[poster] Robert Stephany, Youngsoo Choi |
| 48 | FALCON: An ML Framework for Fully Automated Layout-Constrained Analog
Circuit Design
[paper]
[poster] Asal Mehradfar, Xuzhe Zhao, Yilun Huang, Emir Ceyani, Yankai Yang, Shihao han, Hamidreza Aghasi, Salman Avestimehr |
| 50 | Quantum Boltzmann Machines for Sample-Efficient Reinforcement
Learning
[paper]
[poster] Thore Gerlach, Michael Schenk, Verena Kain |
| 53 | ASTROCO: Self-Supervised Conformer-Style Transformers for Light-Curve
Embeddings
[paper]
[poster] Antony Tan, Pavlos Protopapas, Martina Cádiz-Leyton, Guillermo Cabrera-Vives, Cristobal Donoso-Oliva, Ignacio Becker |
| 54 | Neuromorphic Random Walk for Experimental Phosphate Adsorption
Modeling
[paper]
[poster] Rodrigo P. Ferreira, Rui Ding, Rapti Ghosh, Haihui Pu, Junhong Chen |
| 56 | From Black Hole to Galaxy: Neural Operator Framework for Accretion and
Feedback Dynamics
[paper]
[poster] Nihaal Bhojwani, Chuwei Wang, Hai-Yang Wang, Chang Sun, Elias R Most, Anima Anandkumar |
| 57 | Robustness by Design: Interface Contracts for AI Control in High-Stakes
Physical Systems
[paper]
[poster] Vacslav Glukhov, Georgy Subbotin, Maxim Nurgaliev |
| 59 | Learning IRC-Safe Jet Clustering with Geometric Algebra
Transformers
[paper]
[poster] Gregor Kržmanc, Roberto Seidita, Annapaola de Cosa Reproducibility Prize 🏅 |
| 60 | PI-NAV: Physics-Informed Universal Differential Equations for Enhanced
Nanorobot Navigation through Complex Bio-environments
[paper]
[poster] Tarushri N. S., Prathamesh Dinesh Joshi, Raj Dandekar, Rajat Dandekar, Sreedath Panat |
| 61 | Differentiable Models for Control of Complex Physical Systems: A Case
Study in Laser Pulse Shaping
[paper]
[poster] Denis Ilia, Nihat Ay, Ingmar Hartl, Wolfgang Hillert, Henrik Tünnermann |
| 62 | Power Ensemble Aggregation for Improved Extreme Event AI
Prediction
[paper]
[poster] Julien Collard, Pierre Gentine, Tian Zheng |
| 63 | SoDaDE: Solvent Data-Driven Embeddings with Small Transformer
Models
[paper]
[poster] Gabriel Kitso Gibberd, Jose Pablo Folch, Antonio Del rio chanona |
| 64 | Learning Solution Operators for Partial Differential Equations via Monte
Carlo-Type Approximation
[paper]
[poster] Salah Eddine Choutri, Prajwal Chauhan, Othmane Mazhar, Saif Jabari |
| 65 | GLOW: A Unified Particle Flow Transformer
[paper]
[poster] Dmitrii Kobylianskii, Samuel Van Stroud, Kwok Yiu Wong, Max Hart, Etienne Dreyer, Eilam Gross, Gabriel Facini, Tim Scanlon |
| 66 | Topology-Agnostic Event Reconstruction with Hierarchical Graph Neural
Networks
[paper]
[poster] Nathalie Soybelman, Francesco Armando DI Bello, Nilotpal Kakati, Eilam Gross |
| 67 | Hybrid Physical-Neural Simulator for Fast Cosmological
Hydrodynamics
[paper]
[poster] Arne Thomsen, Tilman Tröster, Francois Lanusse |
| 68 | PIANO: Physics-informed Dual Neural Operator for Precipitation
Nowcasting
[paper]
[poster] Seokhyun Chin, Junghwan Park, Woojin Cho |
| 69 | Lagrangian neural ODEs: Measuring the existence of a Lagrangian with
Helmholtz metrics
[paper]
[poster] Luca Wolf, Tobias Buck, Bjoern Malte Schaefer |
| 70 | An Evaluation of Representation Learning Methods in Particle Physics
Foundation Models
[paper]
[poster] Michael Chen, Raghav Kansal, Abhijith Gandrakota, Zichun Hao, Ngadiuba Jennifer, Maria Spiropulu |
| 71 | Caffarelli Regularity and Hierarchical Phase Boundaries in Diffusion
Model Latent Space
[paper]
[poster] Alexander Lobashev, Dmitry Guskov, Victor Kawasaki-Borruat, Maria Larchenko |
| 72 | Denoising weak lensing mass maps with diffusion model and generative
adversarial network
[paper]
[poster] Shohei D. Aoyama, Ken Osato, Masato Shirasaki |
| 75 | Fourier Neural Operators for Fast Simulation and Inverse Design of
Second-Harmonic Generation in TFLN Waveguide
[paper]
[poster] Valentin Duruisseaux, Robert M. Gray, Siyuan Jiang, Selina Zhou, Robert Joseph George, Kamyar Azizzadenesheli, Alireza Marandi, Anima Anandkumar |
| 76 | Sub-microsecond Transformers for Jet Tagging on FPGAs
[paper]
[poster] Lauri Laatu, Chang Sun, Arianna Cox, Abhijith Gandrakota, Benedikt Maier, Ngadiuba Jennifer, Zhiqiang Que, Wayne Luk, Maria Spiropulu, Alexander Tapper |
| 77 | Inverse Design with Fourier Neural Operators for Quantum System
Control
[paper]
[poster] Anastasia Pipi, G. Nivedha, Valentin Duruisseaux, Myrl G Marmarelis, Taylor Lee Patti, Brucek Khailany, Prineha Narang, Anima Anandkumar |
| 80 | JaxWildfire - A GPU-Accelerated Wildfire Simulator for Reinforcement
Learning
[paper]
[poster]
Ufuk Çakır, Victor-Alexandru Darvariu, Bruno Lacerda, Nick Hawes |
| 81 | RINO: Renormalization Group Invariance with No Labels
[paper]
[poster] Zichun Hao, Raghav Kansal, Abhijith Gandrakota, Chang Sun, Ngadiuba Jennifer, Javier Duarte, Maria Spiropulu Spotlight Talk 🏅 |
| 82 | ComptonINR: Implicit Neural Representations for Fast Modeling of Compton
Telescope Point Spread Functions
[paper]
[poster] Anirudh Kotamraju, Andreas Zoglauer |
| 83 | Selfish Evolution: Making Discoveries in Extreme Label Noise with the
Help of Overfitting Dynamics
[paper]
[poster] Nima Sedaghat, Tanawan Chatchadanoraset, Colin Orion Chandler, Ashish Mahabal, Maryam Eslami |
| 85 | Learning Drosophila ventral furrow formation with graph neural
networks
[paper]
[poster] Shaoxun Huang, Haiqian Yang, Hyuntae Jeong, Xinran O Zhao, Ian Y. Wong, Markus Buehler, Adam C Martin, Ming Guo |
| 87 | The Platonic Universe: Do Foundation Models See the Same Sky?
[paper]
[poster] Kshitij Duraphe, Michael J. Smith, Shashwat Sourav, John F Wu Spotlight Talk 🏅 |
| 88 | A Preliminary Study into the Conceptual Design of Aircraft using
Simulation-Based Inference
[paper]
[poster] Aurelien Ghiglino, Daniel Elenius, Anirban Roy, Ramneet Kaur, Manoj Acharya, Colin Samplawski, Brian Matejek, Susmit Jha, Juan Alonso, Adam D. Cobb Best Poster (by Popular Vote -- tie) 🏅 |
| 89 | AutoHood3D: A Multi‑Modal Benchmark for Automotive Hood Design and
Fluid–Structure Interaction
[paper]
[poster]
Vansh Sharma, Harish Jai Ganesh, Maryam Akram, Wanjiao Liu, Venkat Raman |
| 91 | Enforcing governing equation constraints in neural PDE solvers via
training-free projections
[paper]
[poster] Omer Rochman-Sharabi, Gilles Louppe |
| 92 | Latent Nonlinear Wave Dynamics in Image Datasets and Autoencoder
Reconstructions
[paper]
[poster] Alexey Yermakov, Lillian J. Ratliff, J. Nathan Kutz |
| 93 | Agentic AI at the Advanced Light Source
[paper]
[poster] Thorsten Hellert, João Montenegro, Antonin Sulc |
| 96 | AdaptFNO: Adaptive Fourier Neural Operator with Dynamic Spectral Modes
and Multiscale Learning for Climate Modeling
[paper]
[poster] Hiep V. Dang, Bach Nguyen, Phong C.H. Nguyen, Truong-Son Hy |
| 98 | Capturing Long-Range Intramolecular Interactions with TDiMS for
Interpretable Property Prediction
[paper]
[poster] Lisa Hamada, Akihiro Kishimoto, Junta Fuchiwaki, Kohei Miyaguchi, Indra Priyadarsini, Hirose Masataka, Seiji Takeda, Sina Klampt, Takao Moriyama |
| 99 | Learning Quantum Data Distribution via Chaotic Quantum Diffusion
Model
[paper]
[poster] Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo, Hirotaka Oshima |
| 100 | P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for
Machine Learning Potentials
[paper]
[poster] Shih-Peng Huang, Nontawat Charoenphakdee, Yuta Tsuboi, Yong-Bin Zhuang, Wenwen Li |
| 102 | LINKER: Learning Interactions Between Functional Groups and Residues
With Chemical Knowledge-Enhanced Reasoning and Explainability
[paper]
[poster] PhucPhamHuyThien, Viet Thanh Duy Nguyen, Truong-Son Hy |
| 103 | Granularity Beyond Hardware: Super-Resolution for Enhanced Particle
Reconstruction in Calorimeters
[paper]
[poster] Nilotpal Kakati, Etienne Dreyer, Eilam Gross |
| 104 | Reasoning With a Star: A Heliophysics Dataset and Benchmark for Agentic
Scientific Reasoning
[paper]
[poster] Kevin Lee, Russell Spiewak, James Walsh |
| 105 | SR-Traffic: Discovering Macroscopic Traffic Flow Models with Symbolic
Regression
[paper]
[poster] Simone Manti, Saeed Mohammadian, Martin Treiber, Alessandro Lucantonio |
| 107 | TeBaAb: Text-Based Antigen-Conditioned Antibody Redesign via Directed
Evolution
[paper]
[poster] Cuong Manh Nguyen, Huy-Hoang Do-Huu, Viet Thanh Duy Nguyen, Truong-Son Hy |
| 108 | Squeezed Diffusion Models
[paper]
[poster] Jyotirmai Singh, Samar Khanna, James Burgess |
| 110 | Reliable Parameter Inference for the Epoch of Reionization using
Balanced Neural Ratio Estimation
[paper]
[poster] Diego González-Hernández, Molly Wolfson, Joseph Hennawi |
| 111 | Benchmarking of Neural Operator for rigid-body fluid-structure
interaction
[paper]
[poster] Weiheng Zhong, Hadi Meidani |
| 112 | Learning Data-Efficient and Generalizable Neural Operators via
Fundamental Physics Knowledge
[paper]
[poster] Siying Ma, Mehrdad Momeni Zadeh, Mauricio Soroco, Wuyang Chen, Jiguo Cao, Vijay Ganesh |
| 114 | Mars-Bench: A Benchmark for Evaluating Foundation Models for Mars
Science Tasks
[paper]
[poster] Mirali Purohit, Bimal Gajera, Vatsal Malaviya, Irish Mehta, Kunal Sunil Kasodekar, Jacob Adler, Steven Lu, Umaa Rebbapragada, Hannah Kerner Best Poster (by Popular Vote -- tie) 🏅 |
| 115 | Physics–Preference Aligned Tool-Using Policies for Molecular Design with
Gemma-3 270M
[paper]
[poster] Daoyuan Li |
| 116 | Adaptive Neural Quantum States: A Recurrent Neural Network
Perspective
[paper]
[poster]
[video] Jake McNaughton, Mohamed Hibat-Allah |
| 117 | Physics-aware Discrete Reparameterization with Symmetry-aware Bayesian
Fusion for Multimodal Radiography
[paper]
[poster] Nga Nguyen-Fotiadis, Bradley T. Wolfe, David P Broughton, Zhehui Wang, Nathan A. Debardeleben, Earl Lawrence |
| 118 | EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural
Networks
[paper]
[poster] Tien Dang, Truong-Son Hy |
| 120 | Physics-Informed Neural Networks for Modeling Galactic Gravitational
Potentials
[paper]
[poster] Charlotte Myers, Nathaniel Starkman, Lina Necib |
| 123 | Mechanism discovery with thermodynamically consistent and atom
conserving chemical reaction neural networks
[paper]
[poster] Felix Döppel, Mauro Bracconi, Matteo Maestri |
| 125 | Learning Pairwise Potentials via Differentiable Recurrent
Dynamics
[paper]
[poster] Kenji Komiya, Andrew Kailiang Jin, Ryo Nishikimi, Kunio Kashino |
| 126 | Self-supervised Synthetic Pretraining for Inference of Stellar Mass
Embedded in Dense Gas
[paper]
[poster] Keiya Hirashima, Shingo Nozaki, Naoto Harada |
| 127 | Weight-sharing Transformer quantum states with Suzuki–Trotter
decompositions
[paper]
[poster] Kimihiro Yamazaki, Itsushi Sakata, Takuya Konishi, Yoshinobu Kawahara |
| 128 | Warping Away Nonstationarity: Benefits in Mineral Resource
Estimation
[paper]
[poster] Fabian Leal-Villaseca, Mark D Lindsay, Edward Cripps, Mark Jessell |
| 129 | An Empirical Investigation of Neural ODEs and Symbolic Regression for
Dynamical Systems
[paper]
[poster] Panayiotis Ioannou, Pietro Lio, Pietro Cicuta |
| 131 | Surrogate-Assisted PINNs with Hard Constraints for Heterogeneous
Catalytic Reactor Modeling
[paper]
[poster] Felix Döppel, Mauro Bracconi, Matteo Maestri |
| 132 | Predicting symbolic ODEs from multiple trajectories
[paper]
[poster] Yakup Emre Şahin, Niki Kilbertus, Sören Becker |
| 134 | Differentiable Physics-Neural Models enable Learning of Non-Markovian
Closures for Accelerated Coarse-Grained Physics Simulations
[paper]
[poster] Tingkai Xue, Chin Chun Ooi, Zhengwei Ge, Fong Yew Leong, LI Hongying, Chang Wei Kang |
| 135 | Unsupervised Discovery of High-Redshift Galaxy Populations with
Variational Autoencoders
[paper]
[poster] Aayush Saxena |
| 137 | Fast PINN Eigensolvers via Biconvex Reformulation
[paper]
[poster] Akshay Sai Banderwaar, Abhishek Gupta |
| 140 | Spectroscopic Completeness and Photometric Redshift Performance in
Astronomical Foundation Models
[paper]
[poster] Andrew William Engel, Hailey Widger, Annika Peter, Peter Taylor |
| 142 | Transfer Learning Beyond the Standard Model
[paper]
[poster] Veena Krishnaraj, Adrian E. Bayer, Christian Kragh Jespersen, Peter Melchior |
| 143 | Exoplanet formation inference using conditional invertible neural
networks
[paper]
[poster] Remo Burn, Hubert Klahr, Victor F. Ksoll |
| 144 | Galactification: painting galaxies onto dark matter only simulations
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| 145 | Appa: Bending Weather Dynamics with Latent Diffusion Models for Global
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| 147 | Modeling X-ray photon pile-up with a normalizing flow
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| 148 | GeoGraph: Geometric and Graph-based Ensemble Descriptors for
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| 151 | Sampling 3D Molecular Conformers with Diffusion Transformers
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| 153 | Learning at the Speed of Physics: Equilibrium Propagation on Oscillator
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| 154 | Flexible Gravitational-Wave Parameter Estimation with
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| 155 | Continual Learning for Particle Accelerators
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| 157 | Towards Universal Neural Operators through Multiphysics
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| 158 | A Discrete-Continuous Curriculum Learning (DCCL) Framework for Stable
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| 159 | Uncertainty based Online Ensemble on Non-Stationary Data for Fusion
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| 160 | Edge Machine Learning for Cluster Counting in Next-Generation Drift
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| 161 | Multiscale stochastic parameterization with deep Mori-Zwanzig
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| 162 | Transformer Embeddings for Fast Microlensing Inference
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| 167 | A Benchmarking Framework for AI models in Automotive
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| 169 | Group Averaging for Physics Applications: Accuracy Improvements at Zero
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| 170 | SAIR: Enabling Deep Learning for Protein-Ligand Interactions with a
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| 172 | What Machine Learning Methods is Physics invested in?
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| 174 | Deep Learning for Classification of Low Surface Brightness Galaxies in
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| 176 | Natural gradient descent for improving variational inference based
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| 177 | Extrapolating Phase-Field Simulations in Space and Time with Purely
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| 178 | An LLM-driven framework for cosmological model-building and
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| 179 | Towards Methane Detection Onboard Satellites
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| 180 | Knowledge is Overrated: A zero-knowledge machine learning and
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| 182 | Mind the Gap: Navigating Inference with Optimal Transport Maps
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| 183 | Angular Sparsity Invariant Tilt Series Generation in
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| 184 | Neural Field Transformations for Hybrid Monte Carlo: Architectural
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| 185 | Graph-based Neural Space Weather Forecasting
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[poster] Daniel Holmberg, Ivan Zaitsev, Markku Alho, Ioanna Bouri, Fanni Franssila, Haewon Jeong, Minna Palmroth, Teemu Roos |
| 187 | Toward Complete Merger Identification at Cosmic Noon with Deep
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| 188 | Manifold Learning for Cosmic Structures
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| 189 | Inverse Autoregressive Flows for Zero Degree Calorimeter fast
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| 190 | Seeing the Forest Through the Trees: Knowledge Retrieval for
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| 191 | Uncertainty Quantification for Reduced-Order Surrogate Models Applied to
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[poster] Jonas Erno Katona, Emily Katherine de Jong, Nipun Gunawardena |
| 192 | Power law attention biases for molecular transformers
[paper]
[poster] Jay Shen |
| 194 | One-Shot Transfer Learning for Nonlinear PDEs with Perturbative
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[poster] Samuel Auroy, Pavlos Protopapas |
| 195 | Symbolic Regression Is All You Need: From Simulations to Scaling Laws in
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| 196 | MG-NECOLA: Fast Neural Emulators for Modified Gravity
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| 197 | Hybrid Attention State Space Models for Symbolic Calculation of Squared
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| 198 | Accelerated Blind Denoising of GPR Data via Deep Random
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| 200 | Going with the Speed of Sound: Pushing Neural Surrogates into
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| 202 | Offline Maximizing Minimally Invasive Proper Orthogonal Decomposition
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| 203 | WaveLiT: A Parameter-Efficient Architecture for Neural PDE
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| 205 | Diffusion-Based Electromagnetic Inverse Design of Scattering Structured
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| 206 | High-resolution weak lensing mass mapping from DES-Y3 data using
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[poster] Supranta Sarma Boruah, Michael Jacob, Riya Maiya, Raghav Venkataramanan, Bhuvnesh Jain |
| 207 | Simulation-based inference for neutrino interaction model parameter
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| 208 | Governing Equation Discovery with Relaxed Symmetry Constraints
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| 209 | FOXES: A Framework For Operational X-ray Emission Synthesis
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[poster] Griffin Goodwin, Jayant Biradar, Alison March, Christoph Schirninger, Robert Jarolim, Angelos Vourlidas |
| 211 | An Attention-Based Spatio-Temporal Neural Operator with Uncertainty
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[poster] Vispi Nevile Karkaria, Doksoo Lee, Yi-Ping Chen, Yue Yu, Wei Chen |
| 212 | DNN-based Signal Processing for Liquid Argon Time Projection
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| 214 | Heterogeneous Point Set Transformers for Segmentation of Multiple View
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| 215 | The View From Space: Navigating Instrumentation Differences with
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| 216 | Efficient optimization of COHERENT detector design parameters with the
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| 217 | Moment Estimates and DeepRitz Methods on Learning Diffusion Systems with
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| 218 | Vision Transformers for Cosmological Fields: Application to Weak Lensing
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| 219 | Multi-Modal Masked Autoencoders for Learning Image-Spectrum Associations
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| 220 | The Pareto frontier of resilient jet tagging
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| 221 | An end-to-end pipeline for uncertainty-aware validation of generative
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| 222 | In Search of the Unknown Unknowns: A Multi-Metric Distance Ensemble for
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| 223 | Towards Reliable Sea Ice Drift Estimation in the Arctic: Deep Learning
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| 224 | Revisiting Conditional Whitney Forms: From Structure Preservation to
Physics Recovery
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| 225 | From Descriptions to Chemical Hazards: Predicting Persistence,
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| 226 | Implicit Augmentation from Distributional Symmetry in Turbulence
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| 227 | $\Delta$-ML Ensembles for Selecting Quantum Chemistry Methods to Compute
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| 228 | A Suitable and Interpretable Methodology for FTIR Spectral
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| 229 | Biodiversity Change: A Spatiotemporal Machine Learning Approach to
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| 230 | Generation-Based Multi-Modal Anomaly Detection for Nuclear Fusion Target
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| 232 | Equivariant Compression of Quantum Operator Representations
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| 233 | Uncertainty Quantification of Seismic Imaging Using Neural Posterior
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| 234 | From Images to Physics: Probabilistic Inference of Galaxy Parameters and
Emission Lines via VAE–Normalizing Flows
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| 235 | Generalizing PDE Emulation with Equation-Aware Neural Operators
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| 237 | Learning Spatiotemporal Diffusion Models with Continuous-time PDE
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| 238 | Why is Attention Sparse in Particle Transformer?
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| 239 | Real-Time Anomaly Detection System for Data Quality Monitoring in the
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| 240 | High-Accuracy Neural-Network Quantum States via Randomized Real-Time
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| 241 | Transformers for Deterministic Magnetic Field Control in Particle
Accelerators
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| 242 | Reconstructing Conformal Field Theoretical Composition with
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| 244 | Foam-Agent: A Multi-Agent Framework for Automating OpenFOAM-based CFD
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| 245 | Reinforcement Learning for Ising Models: Datasets and Benchmark
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| 246 | Uncovering Physical Drivers of Dark Matter Halo Structures with
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| 247 | Scalable Machine Learning Analysis of Parker Solar Probe Solar Wind
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| 248 | Electron-Proton Scattering Event Generation using Structured
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| 249 | CIPHER: Scalable Time Series Analysis for Physical Sciences with
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| 251 | Generating Calabi-Yau manifolds with transformers
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| 252 | HEAL-PINN: Physics-Informed Swin Transformer for Dark Matter Studies for
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| 253 | Improving and Assessing Astronomical Light Curve Classifiers with
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| 254 | Uncovering Solar Wind Phenomena with iSAX, HDBScan, Human-in-the-loop
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| 256 | ClearPotential: Learning the Dust-Corrected Potential of the Milky Way
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| 257 | Distributed Element-Local Transformer for Scalable and Consistent
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| 258 | GalaxyScore: Estimating Local Dark Matter Density in Galaxies with Score
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| 259 | Multimodal Generative Flows for LHC Jets
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| 260 | Fake It Till You Make It: Multi-Physics Synthesis Breaks the Data
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| 261 | Developing Surrogates for Epidemic Agent-Based Models via Scientific
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| 262 | Fourier–Thermodynamic Latent Modeling for Temperature-Dependent Plasma
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| 263 | Diffusion Autoencoders with Perceivers for Long, Irregular and
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| 264 | Encoding and Understanding Astrophysical Information in Large Language
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| 265 | From Simulations to Surveys: Domain Adaptation for Galaxy
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| 266 | Sparse Methods for Vector Embeddings of TPC Data
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| 267 | Anomaly Detection in Astrophysics – VAE Separates Interacting Binary
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| 268 | Large Language Model-based Bayesian Optimization for Tokamak
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| 270 | Multi-modal Foundation Model for Cosmological Simulation Data
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| 271 | FoamGPT: Fine-Tuning Large Language Model for Agentic Automation of CFD
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| 272 | Combining datasets with different ground truths using Low-Rank
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| 273 | Solving Inverse Problem Using Physics-Constrained Machine
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| 278 | Designing Optimal Computation Protocols from Fluctuation Response
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| 280 | Scalable Inference for LArTPC Signal Processing with MobileU-Net and
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| 281 | Best of Both Worlds: Bridging Laplace and Fourier for Generalizable and
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| 282 | GT-IBTR-3D: Graph Transformer for 3D Ice-Bed Topography
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| 283 | Sparse Interpretable Deep Learning with LIES Networks for Symbolic
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| 285 | Symbolic Regression via Order-Invariant Embeddings and Sparse
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| 286 | Locality-Sensitive Hashing-Based Efficient Point Transformer for Charged
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| 287 | Lightweight Fourier Neural Operator for Time-Dependent Partial
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| 292 | Scalable and accurate simulations of the Hubbard model with neural
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| 293 | Autonomous Pressure Control in MuVacAS via Deep Reinforcement Learning
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| 296 | Hierarchical Graph Networks for Forecasting Terrestrial Water Storage
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| 297 | Robust Halo Masses using HaloFlow with Domain Adaptation
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| 299 | Inference of Star Formation and Metallicity Histories from Galaxy
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| 300 | Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM
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| 301 | Double Descent and Overparametrization in Particle Physics Data
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| 303 | Embedding Jets with Maximum Manifold Capacity Representations
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| 304 | Active Learning for Machine Learning Driven Molecular Dynamics
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| 305 | Geometry‑Aware Hemodynamics via a Transformer Encoder and Anisotropic
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| 307 | Algebraformer: A Neural Approach to Linear Systems
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| 309 | Scheduled Temporal Loss Weighting for Neural Operators
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| 310 | Assimilation of Sparse Vehicle Trajectories with a Macroscopic Traffic
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| 312 | Evolving graph codes with improved error thresholds for Pauli
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| 313 | Re-envisioning Euclid Galaxy Morphology: Identifying and Interpreting
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| 315 | What We Don't C: Representations for scientific discovery beyond
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| 317 | From Simulation to Survey: Benchmarking Super-Resolution for LSST-like
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| 318 | Physics-Guided Machine Learning For Uncertainty Quantification In
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| 319 | Predictions and Corrections: Neural Predictors with Solver-based
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| 320 | TGLF-SINN: Deep Learning Surrogate Model for Accelerating Turbulent
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| 321 | Phy-SRBench: A Physics Benchmark for Scientific Equation Discovery with
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| 323 | Equivariant Flow Matching for Symmetry-Breaking Bifurcation
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[paper]
[poster]
[video] Fleur Hendriks, Ondřej Rokoš, Martin Doškář, M.G.D. Geers, Vlado Menkovski |
| 324 | Improving Posterior Inference of Galaxy Properties with Image-Based
Conditional Flow Matching
[paper]
[poster] Mikaeel Yunus, John F Wu, Benne Willem Holwerda |
| 327 | Forecasting the Ionosphere from Sparse GNSS Data with Temporal-Fusion
Transformers
[paper]
[poster] Giacomo Acciarini, Simone Mestici, Halil Kelebek, Linnea M. Wolniewicz, Michael Vergalla, Madhulika Guhathakurta, Umaa Rebbapragada, Bala Poduval, Atilim Gunes Baydin, Frank Soboczenski |
| 328 | Deep vs. Shallow: Benchmarking Physics-Informed Neural Architectures on
the Biharmonic Equation
[paper]
[poster]
[video] Akshay Govind Srinivasan, Vikas Dwivedi, Balaji Srinivasan |
| 329 | MITRA: An AI Assistant for Knowledge Retrieval in Physics
Collaborations
[paper]
[poster]
[video] Abhishikth Mallampalli, Sridhara Dasu |
| 330 | Differentiable Ray-Tracing for Optical Particle Detector
Simulation
[paper]
[poster] Omar Alterkait, César Jesús Valls |
| 331 | Reversing The Lens: Using Explainable AI To Understand Human
Expertise
[paper]
[poster] Roussel Rahman, Aashwin Ananda Mishra, Wan-Lin Hu |
| 332 | Neural Deprojection of Galaxy Stellar Mass Profiles
[paper]
[poster] M. J. Yantovski-Barth, Hengyue Zhang, Nolan Smyth, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur, Martin Bureau |
| 333 | ColliderML: A High-Luminosity Detector Simulation Dataset for Machine
Learning Benchmarks
[paper]
[poster] Daniel Murnane |
| 334 | Neural Reduced Potential via Persistent Homology
[paper]
[poster] Yoh-ichi Mototake |
| 335 | Investigating PDE Residual Attentions in Frequency Space for Diffusion
Neural Operators
[paper]
[poster] Medha Sawhney, Abhilash Neog, Mridul Khurana, Arka Daw, Anuj Karpatne |
| 337 | Neural Network for Simulating Radio Emission from Extensive Air
Showers
[paper]
[poster] Pranav Sampathkumar |
| 338 | Intrinsic dimension estimation for Radio Galaxy Zoo using diffusion
models
[paper]
[poster] Joan Font-Quer Roset, Devina Mohan, Anna M M Scaife |
| 340 | Lens-JEPA: Physics Informed Joint Embedding Predictive Architecture for
Gravitational Lensing
[paper]
[poster] J Rishi, Pranath Reddy, Michael W. Toomey, Sergei Gleyzer |
| 341 | The Tokenization Bottleneck: How Vocabulary Extension Improves Chemistry
Representation Learning in Pretrained Language Models
[paper]
[poster] Prathamesh Ashok Kalamkar, Meissane Chami, Ned Letcher, Sahger Lad, Shayan Mohanty |
| 343 | Latent Representation Learning in Heavy-Ion Collisions with MaskPoint
Transformer
[paper]
[poster] Jingzong Zhang, Shuang Guo, Lilin Zhu, Lingxiao Wang, Guo-Liang Ma |
| 344 | Optical Diffraction-based Convolution for Semiconductor Mask
Optimization
[paper]
[poster] Young-Han Son, Dong-Hee Shin, Deok-Joong Lee, Hyun Jung Lee, Hyeonyeong Nam, Tae-Eui Kam |
| 347 | Split-N-Fit: A Differentiable Maximum Likelihood Fit for training neural
networks and performing anomaly detection on data
[paper]
[poster] Philip Harris, Andrzej Novak |
| 348 | Machine Learning Reconstruction of High-dimensional Electronic Structure
from Angle-resolved Photoemission Spectroscopy
[paper]
[poster] Yu Zhang, Yong Zhong, Huy Tran, Shuyi Li, Kyuho Lee, Harold Y. Hwang, Zhi-Xun Shen, Chunjing Jia |
| 349 | Reconstructing the local density field with combined convolutional and
point cloud architecture
[paper]
[poster] Baptiste Barthe--Gold, Nhat-Minh Nguyen, Leander Thiele |
| 351 | FlowLensing: Simulating Gravitational Lensing with Flow
Matching
[paper]
[poster]
Hamees Sayed, Pranath Reddy, Michael W. Toomey, Sergei Gleyzer |
| 352 | Evaluation of Novel Fast Machine Learning Algorithms for
Knowledge-Distillation-Based Anomaly Detection at CMS
[paper]
[poster] Lino Gerlach, Abhishikth Mallampalli, Elliott Kauffman |
| 354 | A Self-Supervised Framework for Robust Multi-Modal Molecular
Representation Learning
[paper]
[poster] Indra Priyadarsini, Seiji Takeda, Lisa Hamada, Sina Klampt, Takao Moriyama |
| 355 | Why Can't Neural Networks Master Extrapolation ? Insights from Physical
Laws
[paper]
[poster] Ramzi Dakhmouche, Hossein Gorji |
| 356 | Preparing stabilizer states via path-aware reinforcement
learning
[paper]
[poster] Krishna Agaram, Siddhant Midha, Vikas K Garg |
| 357 | Graph Neural Networks on One- and Two-Body Integrals for Molecular
Energy Prediction
[paper]
[poster] Juan S. Carrasquilla-Gomez, Rodrigo Vargas-Hernandez |
| 358 | Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric
Forecasting Models
[paper]
[poster] Linnea M. Wolniewicz, Halil Kelebek, Simone Mestici, Michael Vergalla, Giacomo Acciarini, Bala Poduval, Umaa Rebbapragada, Madhulika Guhathakurta, Atilim Gunes Baydin, Frank Soboczenski |
| 359 | Hierarchical Simulation-Based Inference of Supernova Power Sources and
their Physical Properties
[paper]
[poster] Edgar Perez Vidal, Alexander Thomas Gagliano, Carolina Cuesta-Lazaro |
| 360 | IonCast: A Deep Learning Framework for Forecasting Ionospheric
Dynamics
[paper]
[poster] Halil Kelebek, Linnea M. Wolniewicz, Michael Vergalla, Simone Mestici, Giacomo Acciarini, Bala Poduval, Umaa Rebbapragada, Madhulika Guhathakurta, Frank Soboczenski, Atilim Gunes Baydin |
| 361 | Automating High Energy Physics Data Analysis with LLM-Powered
Agents
[paper]
[poster] Eli Gendreau-Distler, Luc Tomas Le Pottier, Chengxi Yang, Dongwon Kim, Joshua Ho, Haichen Wang |
| 362 | Continuous Representations of Baryonic Feedback for Robust Inference
from Multiple Simulation Suites
[paper]
[poster] Ming-Shau Liu, Carolina Cuesta-Lazaro Spotlight Talk 🏅 |
| 365 | Variational Autoencoder with Normalizing flow for X-ray spectral
fitting
[paper]
[poster] Fiona Redmen, Ethan Tregidga, James F. Steiner, Cecilia Garraffo |
| 366 | Surrogate Neural Architecture Codesign Package (SNAC-Pack)
[paper]
[poster] Jason Weitz, Dmitri Demler, Benjamin Hawks, Nhan Tran, Javier Duarte |
Program Committee (Reviewers)
We acknowledge the 491 members of the program committee for providing reviews on a very tight schedule and making this workshop possible. They are listed in alphabetical order below.
Aakash Patil, Aaron Wang, Aashwin Ananda Mishra, Abhijith Gandrakota, Abhilash Neog, Abhinanda Ranjit Punnakkal, Abhinav Sagar, Abhiroop Chatterjee, Abhishek Abhishek, Abhishek Chandra, Abhishikth Mallampalli, Aditya Sengar, Adrian Perez Galvan, Adrian Perez-Suay, Adwaita Janardhan Jadhav, Agnimitra Dasgupta, Ahmed MAZARI, Ahmed Youssef, Aishwarya Jadhav, Aizhan Akhmetzhanova, Ajay Mandyam Rangarajan, Akanksh Shetty, Akshata Kishore Moharir, Alan Aspuru-Guzik, Alessandro Lucantonio, Alex Sun, Alexander J Winkler, Alexander Migala, Alexander Thomas Gagliano, Alexandre Szenicer, Aman Desai, Aman Kumar, Ameya Daigavane, AmirEhsan Khorashadizadeh, Amit Kumar Jaiswal, Anant Wairagade, Andreas Filipp, Andreas Schachner, Andrey E Ustyuzhanin, Anindita Maiti, Ankita Shukla, Anna Dawid, Anna Jungbluth, Annalena Kofler, Antoine Wehenkel, Antonin Sulc, Arkaprabha Bhandari, Arkaprabha Ganguli, Arshad Rafiq Shaikh, Arvind Mohan, Arvind Ramanathan, Arvind Renganathan, Asal Mehradfar, Ashish Kattamuri, Ashley S Dale, Athénaïs Gautier, Atilim Gunes Baydin, Aurelien Dersy, Aviral Prakash, Ayush Prasad, Bariscan Kurtkaya, Barry M Dillon, Batuhan Koyuncu, Ben Meiring, Benjamin D Shaffer, Bharath Ramsundar, Bilal Thonnam Thodi, Biprateep Dey, Biswarup Bhattacharya, Boyu Zhang, Brecht F. Verbeken, Brian Nord, Bruno Raffin, Carolina Cuesta-Lazaro, Cenk Tüysüz, Charuleka Varadharajan, Chen Li, Chen-Nee Chuah, Cheng Soon Ong, Chi Xie, Chin Chun Ooi, Chinmaya Bhagat, Christian Glaser, Christina Reissel, Christine Allen-Blanchette, Christophe Bonneville, Christopher C. Hall, Chuhong Wang, Chuwei Wang, Claire David, Claire Suen, Conrad M Albrecht, Cristiano De Nobili, Dalei Wu, Daniel Murnane, Daniel Serino, Danielle C. Maddix, Daohan Wang, David Rousseau, Debanjan Konar, Deborah Bard, Deep Chatterjee, Deniz Ertuncay, Dhruv V Patel, Dianzhuo Wang, Digbalay Bose, Dikshant Sagar, Dilara ickecan, Dimitra Maoutsa, Dmitry Guskov, Dong Min Roh, Donghun Lee, Eduardo Soares, Edward Berman, Edward Jiang, Elena Pinetti, Elham E Khoda, Elise Özalp, Elyssa Hofgard, Emanuele Usai, Emine Kucukbenli, Engin Eren, Entao Yang, Eoin Quinn, Eric Chagnon, Fabian Gans, Fabian Ruehle, Fadoua Khmaissia, Fatih Dinc, Fatwir Sheikh Mohammed, Favour Nerrise, Felix Döppel, Fernando Romero-Lopez, Feyi Olalotiti, Finn Henry O'Shea, Floriano Tori, Foteini Dervisi, Francesco Alesiani, Francisco Villaescusa-Navarro, Franco Pellegrini, François Rozet, Gabriel Missael Barco, Gabriel Perdue, Gadi Naveh, Gaia Grosso, Gal Oren, Garrett W. Merz, Gary Shiu, George Stein, Georges Tod, Gergana V. Velikova, Gert-Jan Both, Gianni De Fabritiis, Gijs Vermariën, Gilles Louppe, Gourav Khullar, Graham Van Goffrier, Grant M. Rotskoff, Guillaume Lambard, Gérôme Andry, H H C, Haiqian Yang, Hala Lamdouar, Hao Wu, Haowei Ni, Harsh Sharma, Henning Kirschenmann, Hongkyu Yoon, Huanghao Mai, Hunor Csala, Ieva Kazlauskaite, Inbar Savoray, Indra Priyadarsini, Indranil Nayak, Irina Espejo Morales, Ishanu Chattopadhyay, Ivan Grega, Jack Collins, Jacky H. T. Yip, Jaemyoung Lee (🏅 Star Reviewer Prize), Jasleen Dhillon, Jason McEwen, Jay Chan, Jay Taneja, Jean-Luc Fattebert, Jenna Pope, Jeongwhan Choi, Jessica Karaguesian, Jeyashree Krishnan, Jiahe Huang, Jiajing Chen, Jianan Zhou, Jianjun Hu, Jie Bu, Jihan K. Zaki, Jochen Garcke, Joe Germany, Joel Dabrowski, John F Wu, John Michael Martyn, Jonas Spinner, Jordi Tura, Jose Manuel Napoles-Duarte, Joseph Alejandro Gallego Mejia, Joshua Bloom, Joshua Isaacson, Joshua Shen Speagle, Joshua Yao-Yu Lin, Julia Gonski, Junbo Peng, Junichi Tanaka, Junze Liu, Ka Wa Ho, Kai Fukami, Kamilė Lukošiūtė, Kana Moriwaki, Karan Shah, Karla Tame-Narvaez, Katherine Fraser, Kathleen E. Hamilton, Keith Brown, Kenji Komiya, Kevin Greenman, Kiri Wagstaff, Kishansingh Rajput, Krish Desai, Kshitij Tayal, Kunal Sunil Kasodekar, Kuntal Pal, Kunvar Thaman, Kusumakumari Vanteru, Kyriakos Flouris, Kyriakos Hjikakou, Lalit Ghule, Lars Doorenbos, Laura Manduchi, Leander Thiele, Leonid Didukh, Li Yang, Lin Li, Lin Wang, Line H Clemmensen, Lingxiao Wang, Linnea M. Wolniewicz, Lipi Gupta, Liv Helen Våge, Luca Biggio, Lucas Thibaut Meyer, Ludger Paehler, Lukas Heinrich, MD SAJID, MUHAMMAD AMIN NADIM, Mai H Nguyen, Maksim Zhdanov, Mallikarjuna Tupakula, Mangeleer Victor, Manish Marwah, Manuel Morales-Alvarado, Marc Huertas-Company, Marcin Pietron, Maria Cervera, Mariano Javier de Leon Dominguez Romero, Mariel Pettee, Marimuthu Kalimuthu, Marina Meila, Marios Mattheakis, Mary Chriselda Antony Oliver, Mary Idera Salami, Matheus Schossler, Matija Medvidović, Matt L. Sampson, Matt LeBlanc, Matteo Manica, Matthia Sabatelli, Matthieu Blanke, Maurizio Pierini, Maximilian Dax, Medha Sawhney, Micah Bowles, Michael BAUERHEIM, Michael Churchill, Michael R Douglas, Michelle P. Kuchera, Mike Williams, Mikel Landajuela, Milad Ramezankhani, Milind Malshe, Minoo Jafarlou, Mirali Purohit, Miroslav Kubu, Mit Kotak, Mohamed Mehana, Mohammad Shahab Sepehri, Mohammad Sultan, Monika Malik, Mridul Khurana, Muhammad Firmansyah Kasim, Muhammad Khalid, Myungjoon Kim, Nadim Saad, Namid Stillman, Natalie Klein, Navaneetha Krishnan K, Nayantara Mudur, Neel Chatterjee, Negin Forouzesh, Nesar Soorve Ramachandra, Nick McGreivy, Nicole Hartman, Nikolaos Nikolaou, Nils Thuerey, Nischal Reddy Chandra, Nishan Srishankar, Nishant Panda, Nishant Sharma, Nolan Smyth, Nuno Carvalhais, Oleg Savchenko, Olivier Saut, Omar Alterkait, Ondrej Hovorka, Onur Kara, Othmane Rifki, Oz Amram, Ozan Gokdemir, P. Darc, Pankaj Rajak, Pao-Hsiung Chiu, Parag Mallick, Paul Atzberger, Pedro L. C. Rodrigues, Peer-timo Bremer, Pengcheng Xie, Peter McKeown, Peter Melchior, Peter Sadowski, Peter Steinbach, Phaedon Stelios Koutsourelakis, Phan Nguyen, Pietro Vischia, Pinaki Pal, Pradyun Hebbar, Praneeth reddy Amudala Puchakayala, Prathamesh Dinesh Joshi, Pratik Jawahar, Pritthijit Nath, Qiaohao Liang, Quoc Hoan Tran, Raghav Kansal, Raheem Karim Hashmani, Rahul Ghosh, Rajat Arora, Rasmus F. Ørsøe, Raunak Borker, Redouane Lguensat, Remmy Zen, Ricardo Vinuesa, Rishabh Jain, Rodrigo Vargas-Hernandez, Rohith Peddi, Ryan Hausen, Ryan-Rhys Griffiths, Saaketh Bhojanam, Sachin Alexander Reddy, Sajib Acharjee Dip, Saksham Kapoor, Sam Foreman, Sam Vinko, Sampad B Mohanty, Samuel Bright-Thonney, Sandeep Madireddy, Sanghamitra Neogi, Sankalp Gilda, Santosh Parajuli, Sarvesh Kumar Yadav, Satish Chandran, Satpreet Harcharan Singh, Sebastian Kaltenbach, Sebastian Wagner-Carena, Sebastien Fabbro, Shah Nawaz, Shahnawaz Ahmed, Shaokai Yang, Shashank Galla, Shehtab Zaman, Shixiao Liang, Shiyu Wang, Shounak Sural, Shreyas V, Shubhendu Trivedi, Siddhant Midha, Siddharth Mishra-Sharma, Simone Manti, Siu Wun Cheung, Sokratis Trifinopoulos, Soledad Villar, Somya Sharma, Soo Kyung Kim, Sreevani Jarugula, Srinadh Reddy Bhavanam, Srinandan Dasmahapatra, Stephen D. Webb, Stephen Zhang, Steven Dillmann, Sudhakar Pamidighantam, Sudip K Seal, Sui Tang, Sujeet Bhalerao, Supranta Sarma Boruah, Sylvester Kaczmarek, TAMIL ARASAN BAKTHAVATCHALAM, Taimur Muhammad Khan, Takashi Matsubara, Taniya Kapoor, Taoli Cheng, Tarun Kumar, Tarun Narayanan, Tejus Gupta, Theodota Lagouri, Thomas Beckers, Thomas Blum, Tiffany Fan, Till Korten, Tobias Buck, Tomasz Szumlak, Tomo Lazovich, Tri Nguyen, Tsuyoshi Okita, Udit Bhatia, V Ashley Villar, Vacslav Glukhov, Vahe Gharakhanyan, Vaibhavi Singh, Valentina Salvatelli, Vansh Sharma, Vanya Bannihatti Kumar, Ved G. Shah, Vinicius Mikuni, Vishal Dey, Vishal Sudam Jadhav, Vishwa Pardeshi, Vispi Nevile Karkaria, Vitus Benson, Vudtiwat Ngampruetikorn, Wei-Cheng Lee, Wenhao Lu, Wujie Wang, Xian Yeow Lee, Xiang Li, Xiangming Meng, Xiangyang Ju, Xiao-Yong Jin, Xiaohan Yang, Xiaolong Li, Xinyan Li, Yang Liu, Yang Xu, Yangzesheng Sun, Yannik Glaser, Yao Fehlis, Yasin Bakis, Yifan Liu, Yilin Chen, Yin Li, Yingdong Lu, Yingheng Tang, Yingtao Luo, Yiran Wang, Yitian Sun, Yixiao Kang, Yiyi Tao, Yizhi Shen, Yolanne Yi Ran Lee, Youngsoo Choi, Youngwoo Cho, Yu Wang, Yuan-Tang Chou, Yukun Song, Yunxuan Li, Yunyi Shen, Yushang Zhao, ZUKANG YANG, Zefang Liu, Zesheng Liu, Zhong Chen, Zhuo Chen, Zihan Zhu, Zineb Sordo, Zixi Hu, Zixing Song, Zixuan Wang
Call for papers
This workshop brings together physical scientists and machine learning researchers who work at the intersection of these fields by:
- applying machine learning to problems in the physical sciences -- physics, chemistry, astronomy, earth science, biophysics, and related sciences; and
- using physical insights to understand and/or improve machine learning techniques, for instance building hybrid machine learning algorithms that leverage physical models with machine learning blocks to create interpretable and accurate predictive models.
Accepted contributions will be presented during in-person poster sessions during the workshop. Authors will also have the opportunity to upload an optional short video summary alongside their accepted paper on our website. Selected contributions will be offered 15-minute spotlight talks.
Sign up to volunteer as a reviewer: Our community’s success relies on our reviewers – we need you! Please help us ensure a high quality program by registering to review a few papers. Follow this link to nominate yourself and let us know how many papers you would be able to review.
Important dates
- Submission Deadline: Friday, Aug 29, 2025, 23:59 AoE
- Author (accept/reject) notification: Monday, Sept 22, 2025, 23:59 AoE
- Registration support application deadline: October 24, 2025, 23:59 AoE
- Camera-ready paper and poster deadline: November 14, 2025, 23:59 AoE
- Workshop: December 6, 2025
Submission tracks
-
1. Research: We invite contributions on either completed or
high-quality work-in-progress original research in the following areas:
- ML for Physics: Innovative applications of machine learning to the physical sciences; Machine learning model interpretability for obtaining insights into physical systems; Automating/accelerating elements of the scientific process (experimental design, data collection, statistical analysis, etc.).
- Physics in ML: Strategies for incorporating scientific knowledge or methods into machine learning models and algorithms; Applications of physical science methods and processes to understand, model, and improve machine learning models and algorithms.
- Other areas: Any other area related to the subject of the workshop, including but not limited to probabilistic methods that are relevant to physical systems, such as deep generative models, scientific foundation models, probabilistic programming, simulation-based inference, variational inference, causal inference, etc.
3. Perspectives: We invite researchers to present compelling and grounded viewpoints on recent directions and open questions at the intersection of ML and Physical Sciences. This track encourages perspectives on past, present, or future challenges that can stimulate productive and respectful conversations on timely topics that will benefit from the ML4PS workshop's attendees' input. Papers should meet standard scientific rigor, including using evidence and reasoning to support claims, including relevant background and context, and attributing others' work via appropriate citations.
Submission instructions
Submissions should be short papers up to 4 pages in length (excluding references).Organizers
For questions and comments, please contact us at ml4ps@googlegroups.com.
-
Nicole HartmanTechnical University of Munich
-
Garrett MerzUniversity of Wisconsin-Madison
-
Vinicius MikuniLBNL / NERSC
-
Mariel PetteeUniversity of Wisconsin-Madison
-
Sebastian Wagner-CarenaNYU / Simons Foundation
-
Antoine WehenkelApple
Steering Committee
-
Atılım Güneş BaydinUniversity of Oxford
-
Kyle Cranmer
University of Wisconsin-Madison -
Brian NordFermilab
-
Benjamin NachmanLBNL
-
Siddharth Mishra-Sharma Anthropic / Boston University
-
Savannah ThaisColumbia University
Team Members
- Nolan Koblischke (University of Toronto)
Sponsors
Location
NeurIPS 2025 will take place at the San Diego Convention Center, 111 Harbor Dr, San Diego, CA 92101, United States.