Physics-Informed Diffusion Models

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Constrained Diffusion for Data-Scarce Orbital Monte Carlo in Constellation Tasking
Oct 7, 2026Omar Ramadan, Sam Siavoshian, Amir Kashif Saeed +3Constrained Generative Modeling Physics-Informed Diffusion Models
Whiting School of Engineering Johns Hopkins University 3400 N. Charles St. Baltimore, MD 21218, USA
PhyDiCT: Plug-and-Play CT Reconstruction from Sparse X-Rays via Differentiable Rendering and Strong Priors
Oct 7, 2026Weicheng Dai, Shantanu Ghosh, Kayhan BatmanghelichSparse-View CT Reconstruction 3D Medical Imaging
Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
PhysDEM: Physics-Defined Energy-Matching Diffusion for Spatiotemporal Field Generation under Scarce Measurements
Oct 1, 2026Zhenyu Liang, Yining Huang, Yubo Zhao +1Physics-Informed Diffusion Models Physics-Informed Generative Modeling
HKUST · UT Austin
Physical-State-Guided Diffusion Sampling for Full-Waveform Inversion
Sep 14, 2026Chen Min, Haowen Jiang, Zheng Ma +1Physics-Informed Diffusion Models Diffusion-Based Inverse Problems
School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China · CMA-Shanghai, Shanghai Jiao Tong University, Shanghai, China · School of Mathematics and Statistics, Lanzhou University, Lanzhou, China
HNDiff: Haze-Noise Diffusion for Image Dehazing
Aug 11, 2026Jin-Ting He, Fu-Jen Tsai, Yan-Tsung Peng +3Physics-Informed Diffusion Models Diffusion Models for Image Restoration
Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan · National Tsing Hua University, Taiwan · National Chengchi University, Taiwan +1
Physics-Informed Diffusion for Biomechanically Plausible 3D Sign Language Generation
Jul 16, 2026Emanuele Colonna, Moises Diaz, Gennaro Vessio +2Physics-Informed Diffusion Models Human Motion Generation
Department of Computer Science, University of Bari Aldo Moro, Bari, Italy · Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
HPG-Diff: Hierarchical physics-guided diffusion with differentiable connectivity constraints for topology optimization
Jul 8, 2026Jinbo Yang, Mingyue Yuan, Boyuan Zhang +2Topology Optimization Physics-Informed Diffusion Models
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China · Computer Science and Engineering, University of New South Wales, Sydney, 2052, Australia · College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China +1
Least-Action-Guided Diffusion for Physical Extrapolation
Jun 9, 2026Zhongxin Yang, Yuanwei Bin, Xiang I. A. Yang +1Physics-Informed Diffusion Models Physics-Informed Generative Modeling
College of Engineering, Peking University, Beijing 100871, China · Ningbo Institute for Digital Twin, Eastern Institute of Technology, Ningbo 315200, Zhejiang, China · Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo 315200, Zhejiang, China +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.
A physics-informed foundation model for quantitative diffusion MRI
May 29, 2026Zihan Li, Jialan Zheng, Ziyu Li +18Physics-Informed Diffusion Models Medical Imaging Foundation Models
School of Biomedical Engineering, Tsinghua University, Beijing, China. · Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom. · Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China. +4
PIDM-DP: Physics-Informed Diffusion with Dormand-Prince Integration for Chaotic System Identification and State Reconstruction across Multiple Dynamical Regimes
May 26, 2026Shailendra DabralPhysics-Informed Diffusion Models Nonlinear System Identification
Indian Institute of Technology Indore
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
EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesis
May 19, 2026Alzahra Altalib, Chunhui Li, Haytham Ahmad Alewaidat +3Physics-Informed Diffusion Models Cone-Beam CT
School of Science and Engineering, University of Dundee UK · Faculty of Applied Medical Sciences, Jordan University of Science and Technology · Experia Healthcare, Jordan +1
Learning Interpretable PDE Representations for Generative Reconstructions with Structured Sparsity
Apr 26, 2026Valerie Tsao, Nathaniel Chaney, Manolis VeveakisPhysics-Informed Diffusion Models Sparse Recovery
Department of Civil & Environmental Engineering, Duke University, NC, USA.
Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation
May 28, 2025Yi Zhang, Peng Wang, Difan ZouPhysics-Informed Diffusion Models Diffusion Model Distillation
Institute of Data Science · Department of Computer and Information Science · Institute of Data Science & School of Computing and Data Science

Oct 7, 2026, astro-ph.IMJ/K move · Enter open · S save

Constrained Diffusion for Data-Scarce Orbital Monte Carlo in Constellation Tasking

Omar Ramadan, Sam Siavoshian, Amir Kashif Saeed, Benjamin A. Johnson+2

Whiting School of Engineering Johns Hopkins University 3400 N. Charles St. Baltimore, MD 21218, USA

Constrained Generative Modeling Physics-Informed Diffusion Models LEO Satellite Networks

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Constellation Monte Carlo results depend on the orbital population used to evaluate a tasking policy. With scarce reference trajectories, replay limits geometric diversity, while independent orbital-element jitter can violate physical constraints. We study constrained diffusion for orbital-population augmentation. A force-conditioned diffusion model learns a 13-dimensional orbital prior, recovering semimajor axis from perigee altitude and eccentricity; Basilisk propagates each sample under one of five force-model tiers. Using 800 reference trajectories, we compare diffusion with jittered bootstrap, per-tier Gaussian mixtures, and a conditional variational autoencoder, and evaluate distributional fidelity, support shift, classical astrodynamics diagnostics, and 4,000 paired GoDSAT-compatible campaigns. The largest diffusion model achieves held-out trajectory MMD of 0.0171 +/- 0.0239, similar to bootstrap (0.0170) and the mixture (0.0198), but samples farther from training priors (median nearest-training distance 2.7 versus 0.10 standardized units). All generators fail to match the shifted-blind population (classifier AUC 0.991-0.998). Endpoint-conditioned samples satisfy Lambert boundaries but have greater interior error than the matched Lambert reference (29.2 versus 5.7 km mean RMSE). Residual diffusion improves selected sparse forecasts and catalog-mode recall but does not outperform classical estimators on custody ranking. In a fixed 16-satellite configuration, diffusion yields similar mean custody to replay and bootstrap, while the force-tier mixture shifts custody by about 5.5 percentage points. Constrained diffusion supports local, in-support augmentation but cannot replace orbital dynamics or serve as an operational posterior. Orbital-population construction is a consequential source of uncertainty in constellation analysis.

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