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arXivPhysics-informed ML & PDEs

PDE-JEPA: Predictive Representation Learning of Latent Dynamics Modeling for Parametric PDEs

PDE-JEPA introduces predictive masked-latent pretraining with geometry projection and structured latent predictors for parametric PDE dynamics, reducing errors by 33.4% in-distribution and 51.4% on unseen parameters.

Zhentao Tan, Jianrong Zhang, Ruijie Quan, Yi Yang

Published Sep 28, 2026 · 0 citations · ▲ 53 on Hugging Face · Code ★ 9

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NeurIPS 2026U BarcelonaGoogle Deepmind / HarvardPhysics-informed ML & PDEs

Generalizable Physics Simulation through Compositional Energy Minimization

Alexandru Oarga, Yilun Du

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Massachusetts Institute of TechnPhysics-informed ML & PDEs

GLOBE: Accurate Surrogates for Boundary-Driven PDEs via Domain-Inspired Architectures and Equivariance

Peter Sharpe

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026The University of Electro-CommunPhysics-informed ML & PDEs

Sliced Wasserstein Meets Quantum Optics: Provable Wavefunctions Tomography with Scarce Noisy Measurements

Takahiro Kajisa

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Koreakorea universityPhysics-informed ML & PDEs

Gauge-Symmetric Dual Lagrangian Frameworks for Born-Oppenheimer Molecular Dynamics

Sungwoo Park, Jongwon Lee, Jiwoong Kim, Hyung-sik Yoon

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026TsinghuaPrincetonPhysics-informed ML & PDEs

Discovering Phase Space Structure in Learned Hamiltonian Systems

Jiayin Liu, Yulong Yang, Vineet Bansal, Christine Allen-Blanchette

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Fujitsu Research of AmericaFujitsu Research and DevelopmentPhysics-informed ML & PDEs

NeuralBES: A Differentiable, Control-Aware Emulator for Scalable Building Energy Modeling

Ting-Yu Dai, Takuya Kurihana, Wing Yee Au, YUNG WONG

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U OxfordPhysics-informed ML & PDEs

Trajectory-Consistent Dropout for Uncertainty Decomposition in Hamiltonian Neural Networks

Stephen J Roberts, Yuki Tachibana

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Exponent Inc.Physics-informed ML & PDEs

Train on the Sphere, Deploy on the Hill: Closed-Form-Anchored Surrogates for Real-Terrain Boundary-Integral Equations

Stephane Zsoldos, Therice Morris, Varundev Sukhil, Benjamin Wetherfield

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026HunanTsinghua University, TsinghuaChangsha University of Science aPhysics-informed ML & PDEs

GEMS-3D: A Large-Scale 3D Gravity, Electrical, Magnetic, and Seismic Earth Simulation Dataset for Multimodal Geophysical Learning

Yonghao Wang, Meijia Huang, Wenkai Lu, Zhuo Jia and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SemyungPhysics-informed ML & PDEs

PINNBench: A Benchmark and Evaluation Study of Training Policy Selection in Hybrid PINN-Operator Solvers

Suan Lee, Namhyeon Kim, Dongmin Jin

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026KoreaPhysics-informed ML & PDEs

Symplectic Parallel Scan: A Neural Hamiltonian Framework for Accelerated Scientific Simulation

Sungwoo Park

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Science and Technology of ChinHunanPhysics-informed ML & PDEs

PI-EDG: Physics-Informed Full-Space Electron Density Generation from Molecular Geometry

Jiahang Shen, Hongxin Xiang, Zhixiang Cheng, Keke Chen and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026PrincetonPhysics-informed ML & PDEs

Frequency-Structured Hamiltonian Neural Network for Multi-Timescale Dynamics

Yaojun Li, Yulong Yang, Christine Allen-Blanchette

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SLAC National Accelerator LaboraUniversity College London, UniveTechnische Universität MünchenUCLHumboldt Universität BerlinPhysics-informed ML & PDEs

Neural Scaling Laws in Particle Jets

Matthias Vigl, Nikita Pond, Nicole Hartman, Jackson Barr and 10 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SichuanPhysics-informed ML & PDEs

ALETHEIA: A Multi-Frequency Eddy Current Pulsed Thermography Dataset for Neural Operator Learning in Nondestructive Testing

Changbin Sun, Xiaojie, Xiaotian Chen, Yuankai Wu

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026NorthwesternNorthwestern University, NorthweU FloridaPhysics-informed ML & PDEs

Axiomatic World Modeling for Physics Reasoning

Xinye Yang, Zhenyang Liu, Yuxuan Wang, Yuanyuan Lei

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026AXXX, Institute of Numerical MatNorth-Eastern FederalLaboratory of Computational InteThe Chinese University of Hong KInstitute of Numerical MathematiPhysics-informed ML & PDEs

Transolver-GMsFEM: A Hybrid Framework for High-Contrast Multiscale PDEs on Irregular Grids

Alexander Rudikov, Sergei Stepanov, Vladimir Fanaskov, Eric Chung and 2 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026SpotlightU CambridgePrincetonPhysics-informed ML & PDEs

Learning Discrete Riemannian Metrics for Physical Fields with Cochain-Frame Equivariance

Dongzhe Zheng, Christine Allen-Blanchette

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026LMU MunichTU MunichMarburguniversityPhysics-informed ML & PDEs

Conformal Prediction for Time-Dependent PDEs

Joshua Stiller, Annika Schneider, Eyke Hüllermeier

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Korea Institute for Advanced StuMITKAISTPhysics-informed ML & PDEs

Variational Monte Carlo for Quantum Excited States via Nested Low-Rank Approximation

Minchan Jeong, Jongha (Jon) Ryu, Se-Young Yun, Gregory Wornell

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026ResolutionCSIRO TechnologyU MelbourneCSIROPhysics-informed ML & PDEs

Susceptibilities for Neural Networks Learning from Physical Data

Rohan Hitchcock, Gary W Delaney, Jonathan H Manton, Richard Scalzo and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026N2TPNanyang TechnologicalB0Labs, N2TP TechnologyN2TP Technology Solutions JSCPosts & Telecommunications IPhysics-informed ML & PDEs

SympFNO: Structure-Preserving Fourier Neural Operators for Physical Surrogate Modeling

Luong Doan, Duc H Nguyen, Khanh N Quoc, Phan Quoc Hung Mai and 6 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Chinese university of HongkongThe Chinese University of Hong KPhysics-informed ML & PDEs

Neural Operator-based Curriculum Learning for Physics-Informed Neural Networks

Lingshi MENG, Haosen Shi, Sinno Pan

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Washington StateHanoi University of Science and U Texas at AustinPennsylvania StateWashington State University, PulPhysics-informed ML & PDEs

CoupledFlow: One-Step Neural Operators for Coupled Multi-Physics PDEs

Trong Khiem Tran, Long M Bui, Phi Le Nguyen, Mrinal K Sen and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Fujian University of TechnologyShenzhen Institutes of Advanced Shenzhen Institutes of Advanced Physics-informed ML & PDEs

DNS-Calibrated Local Stochastic Transition Closure for PDE-Free Long-Horizon Turbulence Diffusion

Zhuoer Lin, Wenwu He, Congcong Liu, Zhuo-Xu Cui

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026ETH ZurichETHZIBM Research EuropeSwiss Federal Institute of TechnPhysics-informed ML & PDEs

Phaedra: Learning High-Fidelity Discrete Tokenization for the Physical Sciences

Levi Lingsch, Georgios Kissas, Johannes Jakubik, Siddhartha Mishra

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Gran Sasso Science InstituteThe University of EdinburghPhysics-informed ML & PDEs

Simplifying Transformer-Based U-Net Neural Physics Simulators

Pietro Sittoni, Francesco Tudisco

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026OralShanghai Jiao TongShanghai JiaotongPhysics-informed ML & PDEs

DIAGNO: Diagonal Spherical Neural Operators for Heterogeneous Earth Dynamics Modeling

Herui Li, Bin Lu, Haonan Qi, Lei Zhou and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Colorado School of MinesNayantaPhysics-informed ML & PDEs

KerONet: A Single Softmax Readout Suffices for Physics-Informed Operator Learning

Seth Dale, Carolyn Koh, Dinesh Mehta

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026CaltechCalifornia Institute of TechnoloMITMassachusetts Institute of TechnU WashingtonPhysics-informed ML & PDEs

CFC26: Building Evaluations for Deployment in Sonar-Based Fish Counting

Madison Van Horn, Suzanne Stathatos, Sevan Brodjian, Justin Kay and 5 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026National University of Defense TPekingNorthwest PolytechnicalHong Kong PolytechnicTiangongPhysics-informed ML & PDEs

Architecture-Embedded Physics Priors for Mitigating Spectral Bias in Physics-Informed Neural Networks

Zhuo Zhang, Li Zhang, Ying Miao, Hongzong LI and 3 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Texas TechPhysics-informed ML & PDEs

Stop Quantum Machine Learning; do AI-for-Quantum instead

ziqing Guo, Ziwen Pan

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Université de Bretagne SudUniversité Bretagne SudPhysics-informed ML & PDEs

BESS-Bench: Benchmarking Spectral Representations for Be-Star Variability

Matthieu Le Lain, Sébastien Lefèvre

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Purdue, PurduePhysics-informed ML & PDEs

A Locality-Aware Surrogate for Natural-Gradient Descent in Quantum Optimization

Md Mobasshir Arshed Naved, Wenbo Xie, Wojciech Szpankowski, Ananth Grama

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Indian Institute of ScienceIndian Institute of Science, IndPhysics-informed ML & PDEs

Adaptive multiscale operator correction via learned spectral subspace and physics-informed optimization.

Subham Patel, Himanshu Pandey, RATIKANTA BEHERA

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Massachusetts Institute of TechnMITHarvardNew YorkInstituto de Astrofisica De CanaPhysics-informed ML & PDEs

Learning What's Real: Disentangling Signals and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics

Pablo Mercader-Perez, Carolina Cuesta Lazaro, Daniel Muthukrishna, Jeroen Audenaert and 4 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SuwonThe University of SuwonLinyiPhysics-informed ML & PDEs

MANGO:Multi-Angle Neural Gated Operators for Chirp-Perturbed PDEs

Yunlong Zhu, Zunwei Fu, Zheng Wang, EUN-HU KIM

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Institute of Computing Technolog, Chinese Academy of SciencesU Chinese Academy of ScienceInsititute of Computing TechnoloPhysics-informed ML & PDEs

Verigrad: Verification-Driven Multi-Agent GPU Kernel Generation for High-Order MLIP Derivatives

yao liu, Yuanchang Zhou, Hongtao Xu, Mingzhen Li

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SolverXKorea Advanced Institute of ScieSeoul NationalTelepixArizona StatePhysics-informed ML & PDEs

PDE-PFN: Prior-Data Fitted Neural PDE Solver

Jaehyeon Park, Mingu Kang, Dongseok Lee, Woojin Cho and 4 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026StanfordInstitute of Science and TechnolEcole des MinesTechnion - Israel Institute of TPhysics-informed ML & PDEs

Symplectic Reck: In-Situ Learning of Gaussian Quantum Operations

Janet Zhong, Renwen Yu, Charles Roques-Carmes, Paul-Alexis MOR and 3 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Hewlett Packard EnterpriseHPE LabsU RochesterLaboratory for Laser EnergeticsPhysics-informed ML & PDEs

End-to-End Differentiable Diffusion Conditioning for Physics-Informed Optimization

Ricardo Luna Gutierrez, Vineet Gundecha, Rahman Ejaz, Varchas Gopalaswamy and 4 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Hewlett Packard EnterpriseHPE LabsU RochesterLaboratory for Laser EnergeticsPhysics-informed ML & PDEs

Inverse Modeling for Laser Pulse Shape Design in Inertial Confinement Fusion

Ricardo Luna Gutierrez, Vineet Gundecha, Rahman Ejaz, Varchas Gopalaswamy and 4 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026King's College LondonJohns HopkinsPhysics-informed ML & PDEs

Macrocanonical Generator Networks: data-efficient neural surrogates for amortized physics simulation

Niall Jeffrey, Benjamin Wandelt

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026PrincetonPhysics-informed ML & PDEs

Neural Modal Decomposition: Architectural Priors from Observables

A neural framework learns pole-residue modal decomposition from system observables alone, generalizing to unseen port counts and recovering physical eigenmodes without modal supervision.

Juho Park, Kaushik Sengupta

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
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NeurIPS 2026The University of TokyoPhysics-informed ML & PDEs

Exactness Matters for Physical Rule Enforcement

Exact physical projection improves autoregressive forecasts when operators match target geometry, but approximate enforcement can increase rollout error and should be benchmarked by alignment.

Bum Jun Kim

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 15 of 20 reviewers recommend it
lenient 3/5
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NeurIPS 2026INRIAINRIA + University Grenoble-AlpeU GrenoblePhysics-informed ML & PDEs

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training

OGAS actively samples challenging PDE configurations via a diffusion model to reduce worst-case surrogate error with minimal overhead.

Pierre Cesar, Sofya Dymchenko, Abhishek Purandare, Bruno Raffin

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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AI panel: 12 of 20 reviewers recommend it
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NeurIPS 2026Technical University of MunichForschungszentrum Juelich GmbHTechnical University of Munich /Physics-informed ML & PDEs

Transferable SCF-Acceleration through Solver-Aligned Initialization Learning

Solver-Aligned Initialization Learning differentiates through SCF solvers to train transferable ML initial guesses, reducing iterations by up to 37% on molecules up to 10× larger than training data.

Eike S. Eberhard, Viktor Kotsev, Timm Güthle, Stephan Günnemann

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 18 of 20 reviewers recommend it
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NeurIPS 2026The Hong Kong University of ScieSouthwest JiaotongThe Hong Kong University of SciePhysics-informed ML & PDEs

Reformulating Neural Operators in $d+1$ Dimensions for Embedding Evolution

Reformulating neural operators in d+1 dimensions via auxiliary embedding evolution achieves lowest relative L2 error across benchmarks without brute-force scaling.

Haoze Song, Zhihao Li, Xiaobo Zhang, Zecheng Gan and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 11 of 20 reviewers recommend it
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NeurIPS 2026The University of MelbourneNational University of SingaporeU MelbourneIFLYTEK CO.LTD.Physics-informed ML & PDEs

PhysGuard: Fisher-Guided Gradient Projection for Sim-to-Real Neural PDE Surrogates

PhysGuard uses Fisher-guided gradient projection to adapt neural PDE surrogates to real data while preserving physics-critical parameters, cutting low-frequency error by up to 32% under severe domain shift.

Changjian Zhou, Junfeng Fang, Negin Yousefpour, peng wu and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 15 of 20 reviewers recommend it
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NeurIPS 2026PurduePekingPhysics-informed ML & PDEs

Ultra Fast PDE Solving via Physics Guided Few-step Diffusion

Phys-Instruct distills diffusion PDE solvers into few-step generators with explicit physics guidance, achieving orders-of-magnitude faster inference and over 8x lower PDE error.

Xiangrui Cindy Kong, Yueqi Wang, Haoyang Zheng, Weijian Luo and 1 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
lenient 4/5
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NeurIPS 2026ZhejiangU MinnesotaPhysics-informed ML & PDEs

StreamPhy: Streaming Inference of High-Dimensional Physical Dynamics via State Space Models

StreamPhy enables real-time streaming inference of high-dimensional physical fields from irregular sparse measurements via adaptive encoders and state-space updates, outperforming diffusion baselines by up to 48% accuracy and 20-100x speed.

Panqi Chen, Yifan Sun, Shikai Fang, Xiao Fu and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 14 of 20 reviewers recommend it
lenient 5/5
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NeurIPS 2026The Chinese University of HongkoSEAS, HarvardPhysics-informed ML & PDEs

Optimal Ansatz-free Hamiltonian Learning In Situ

A control-free, ancilla-free algorithm learns ansatz-free Hamiltonians via Pauli preparations with optimal total evolution time scaling and resolution depending only on the norm bound.

Taiqi Zhou, Weiyuan Gong

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 10 of 20 reviewers recommend it
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NeurIPS 2026Seoul NationalYonseiPhysics-informed ML & PDEs

Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations

NVQLS proposes a hybrid quantum-classical unsupervised operator learning framework using a Legendre-Galerkin formulation to solve parametric PDEs with improved accuracy and theoretical speedups.

Chanyoung Kim, Myeonghwan Seong, Kim Yujin, Daniel Kyungdeock Park and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 6 of 20 reviewers recommend it
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NeurIPS 2026U WaterlooPhysics-informed ML & PDEs

Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum States

Stochastic reconfiguration acts as ridge regression filtering finite-sample noise in overparameterized neural quantum states, and multi-shift averaging lowers validation risk and variance.

Tak Hur

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 13 of 20 reviewers recommend it
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NeurIPS 2026South China University of TechnoZhejiangPhysics-informed ML & PDEs

Derived Fields Preserve Fine-Scale Detail in Budgeted Neural Simulators

Derived-Field Optimization selects carried physical fields and allocates storage budgets to preserve fine-scale detail in budgeted neural simulators, significantly improving fidelity before rollout even on PDEBench.

Wenshuo Wang, Fan Zhang

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 14 of 20 reviewers recommend it
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NeurIPS 2026Harbin EngineeringPhysics-informed ML & PDEs

APPSolver: Adaptive Patch Partitioning for Point-Wise Ship Flow Prediction on Unstructured Meshes

APPSolver introduces adaptive quadtree patch partitioning for efficient point-wise ship flow prediction, reducing computation versus uniform patches but not universally outperforming persistence baselines.

Wenhua Huo, Fenglei Han, Wangyuan Zhao, Xiao Peng and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
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NeurIPS 2026ZhejiangThe University of TokyoUTokyoPhysics-informed ML & PDEs

M$^3$: Reframing Training Measures for Discretized Physical Simulations

M³ balances training measures via multi-scale Morton partitioning to reduce measure-induced bias, cutting volumetric simulation errors up to 4.7× and outperforming high-resolution training under aggressive subsampling.

Yuan Mei, Xingyu Song, Xiaowen Song, Naoya Takeishi

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 16 of 20 reviewers recommend it
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NeurIPS 2026ParisTechBrownU TorontoBasis Research InstituteMila, Université de MontréalPhysics-informed ML & PDEs

Wasserstein residuals: Learning Gradient Flows from Population Dynamics

A residual-based continuity loss for Wasserstein gradient flows yields a simulation-free stitching method robust to sparse observations and state-of-the-art on trajectory inference benchmarks.

Markus Heinonen, Yair Shenfeld, Ricardo Baptista, Daniel Waxman and 3 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 11 of 20 reviewers recommend it
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