Physics-Informed Generative Modeling

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13 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
Jun 2, 2026Cheng Jiang, Sitian Qian, Kevin Pedro +3Physics-Informed Generative Modeling Flow Matching
School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3FD, United Kingdom · Department of Physics, University of Wisconsin-Madison, Madison, WI 53706, USA · Fermi National Accelerator Laboratory, Batavia, IL 60510, USA +2
Data Enrichment for Symbolic Regression Using Diffusion Models
May 31, 2026Simon De Reuver, Tamas Kristof Toth, Teddy LazebnikPhysics-Informed Diffusion Models Physics-Informed Generative Modeling
Department of Computing, Jönköping University, Jönköping, Sweden · Department of Information Science, University of Haifa, Haifa, Israel
Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks
May 29, 2026Gyeonghoon Ko, Juho LeePhysics-Informed Diffusion Models Physics-Informed Generative Modeling
1Korea Advanced Institute of Science and Technology, Korea.
Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction
May 21, 2026Ziyuan Zhu, Keyu Hu, Zhifei Chen +10Physics-Informed Generative Modeling Physics-Informed ML
School of Advanced Manufacturing Engineering, Nanjing University, Suzhou, 215163, China. · National Laboratory of Solid State Microstructures, Nanjing University, Nanjing, 210093, China. · Suzhou Acoustics Industry Technology Research Institute Co., Ltd., Suzhou, 215513, China. +8
Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment
May 20, 2026Haozhe Jia, Pengyu Yin, Wenshuo Chen +6Physics-Informed Diffusion Models Physics-Informed Generative Modeling
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Shandong University, Jinan, China · Data61/CSIRO, Australia +5
Physics Guided Conditional Diffusion Framework for Generative Inverse Design of Manufacturable Metasurface based Absorbers
May 19, 2026Vineetha Joy, Jamshed Palai, Satwik Sahu +3Physics-Informed Generative Modeling Physics-Informed ML
Centre for Electromagnetics, CSIR-National Aerospace Laboratories, Kodihalli, Bangalore 560017 · Birla Institute of Technology and Science, Pilani, Rajasthan 333031 · Indian Institute of Technology, Bombay, Maharashtra 400076
PhyWorld: Physics-Faithful World Model for Video Generation
May 19, 2026Pu Zhao, Juyi Lin, Timothy Rupprecht +10Physics-Informed Generative Modeling Physical Consistency in Video Generation
Northeastern University · University of Georgia · Tulane University +1
NEWTON: Agentic Planning for Physically Grounded Video Generation
May 18, 2026Yuxiang Feng, Juncheng Wang, Chao Xu +7Physics-Informed Generative Modeling Physical Consistency in Video Generation
Zhejiang University · The Hong Kong Polytechnic University · IROOTECH TECHNOLOGY +1
GenAI-FDIA: Physics-Informed Generative Models for False Data Injection Attacks
May 15, 2026Mohammad A. Razzaque, Muta Tah HiraCyber-Physical Systems Physics-Informed Generative Modeling
School of Computing, Engineering and Digital Technologies, Teesside University, UK · Smartifier Ltd, Stockton-on-Tees, UK
PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics
May 5, 2026Hao Zhou, Rui Zhang, Han Wan +1Physics-Informed Generative Modeling Uncertainty Quantification
Gaoling School of Artificial Intelligence, Renmin University of China
PhyCo: Learning Controllable Physical Priors for Generative Motion
Apr 30, 2026Sriram Narayanan, Ziyu Jiang, Srinivasa Narasimhan +1Physics-Informed Generative Modeling Physical Consistency in Video Generation
1Carnegie Mellon University · 2NEC Labs America · 3UC San Diego
Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training
Feb 10, 2026Che-Chia Chang, Chen-Yang Dai, Te-Sheng Lin +2Physics-Informed Generative Modeling PDE Surrogate Modeling
Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Hsinchu, Taiwan · Department of Applied Mathematics National Yang Ming Chiao Tung University Hsinchu, Taiwan · Department of Applied Mathematics National Yang Ming Chiao Tung University Hsinchu, Taiwan National Center for Theoretical Sciences National Taiwan University Taipei, Taiwan
Multiparameter Uncertainty Mapping in Quantitative Molecular MRI using a Physics-Structured Variational Autoencoder (PS-VAE)
Feb 3, 2026Alex Finkelstein, Ron Moneta, Or Zohar +4Magnetic Resonance Imaging Uncertainty Quantification
School of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel · Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel · School of Biochemistry, Neurobiology, and Biophysics, Tel-Aviv University, Tel-Aviv, Israel +1
Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems
Aug 5, 2025Jan Tauberschmidt, Sophie Fellenz, Sebastian J. Vollmer +1Physics-Informed Generative Modeling Flow Matching
DSA, German Research Center for Artificial Intelligence (DFKI) · Department of Computer Science, University of Kaiserslautern–Landau (RPTU) · Department of Mathematics, Imperial College London
PhyMAGIC: Physical Motion-Aware Generative Inference with Confidence-guided LLM
May 22, 2025Siwei Meng, Yawei Luo, Ping LiuPhysics-Informed Generative Modeling Physical Consistency in Video Generation
Department of Computer Science & Engineering, University of Nevada, Reno, USA · School of Software Technology, Zhejiang University, China

Jun 2, 2026, hep-exJ/K move · Enter open · S save

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

Cheng Jiang, Sitian Qian, Kevin Pedro, Oz Amram+2

School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3FD, United Kingdom · Department of Physics, University of Wisconsin-Madison, Madison, WI 53706, USA · Fermi National Accelerator Laboratory, Batavia, IL 60510, USA+2

Physics-Informed Generative Modeling Flow Matching Surrogate Modeling One-Step Generative Modeling Physics-Informed ML Physics-Based Simulation

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High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4. Flow matching and diffusion-based generative models have become leading approaches for high-dimensional fast simulation because of their sample quality, but typically require

{\cal O}(100)

function evaluations at inference and often rely on auxiliary networks to constrain global observables, compromising streamlined end-to-end generation. We introduce a unified framework that improves the balance between speed, shower quality, and physics fidelity. The method combines: (i) an average velocity field integrator that enables sampling in one or a few evaluations; (ii) a learned generative prior in shower space, constructed from data rather than random noise; and (iii) physics-guided loss terms that impose inductive biases on key observables during training. These elements are training time regularizers, preserving end-to-end inference with no additional cost. With only one or a few evaluation steps, the model achieves shower quality competitive with state-of-the-art flow and diffusion approaches, tested on several public high granularity calorimeter datasets. The results demonstrate inter-layer shower structure consistent with the underlying physics, providing a strong candidate for future fast simulation workflows.

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Latest papers 45