Hugging Face Trending Papers

The Right Measure for Physics-Constrained Generation: A Co-Area Correction for Posterior-Consistent PDE Inverse Problems

Generative models -- diffusion and flow matching -- are increasingly used to solve partial differential equation (PDE) inverse problems, enforcing the governing physics as a \emph{hard constraint} (via projection or guidance) and reporting the resulting samples as a Bayesian posterior with calibrated uncertainty. We show that this widely adopted recipe samples the wrong distribution.

arXiv Machine Learning

Jun 4

The Right Measure for Physics-Constrained Generation: A Co-Area Correction for Posterior-Consistent PDE Inverse Problems

arXiv:2606. 04804v1 Announce Type: new Abstract: Generative models -- diffusion and flow matching -- are increasingly used to solve partial differential equation (PDE) inverse problems, enforcing the governing physics as a \emph{hard constraint} (via projection or guidance) and reporting the resulting samples as a Bayesian posterior with calibrated uncertainty.

By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao

diffusion safety

arXiv Machine Learning

Aug 7

Dual-space posterior sampling for Bayesian inference in constrained inverse problems

arXiv:2603. 00393v2 Announce Type: replace-cross Abstract: Inverse problems constrained by partial differential equations are often ill-conditioned due to noisy, incomplete data or inherent non-uniqueness.

By Ali Siahkoohi, Kamal Aghazade, Ali Gholami

benchmarks

arXiv Machine Learning

Jun 2

Measurement Geometry and Design for Trustworthy Generative Inverse Problems

arXiv:2606. 02309v1 Announce Type: new Abstract: Generative models are increasingly used as priors for inverse problems, but their ability to produce realistic images creates a basic trust problem: a plausible reconstruction may be supported by the measurements, or it may be filled in by the prior along unobserved directions.

By Pengfei Jin, Na Li, Quanzheng Li

safety

arXiv AI

Jun 30

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation

arXiv:2505. 22391v2 Announce Type: replace-cross Abstract: Modeling physical systems in a generative manner offers several advantages, including the ability to handle partial observations, generate diverse solutions, and address both forward and inverse problems.

By Yi Zhang, Peng Wang, Difan Zou

diffusion efficiency benchmarks

arXiv Machine Learning

Aug 4

Sampling Decisions: Exact Path-Space Control for Physics-Informed Generative Sampling

arXiv:2503. 14549v4 Announce Type: replace Abstract: Scientific generative models must turn tractable local decisions into globally correlated samples that respect physical constraints.

By Michael Chertkov, Hamidreza Behjoo, Sungsoo Ahn

arXiv Machine Learning

Jun 2

Hallucination-Aware Diffusion Sampling for Inverse Problems via Robust Prior Updates

arXiv:2606. 02331v1 Announce Type: cross Abstract: Diffusion-based inverse problem solvers can produce realistic reconstructions, but realism alone does not ensure that the recovered details are supported by the measurement.

By Pengfei Jin, Yiqi Tian, Kailong Fan, Bingjie Qi, Quanzheng Li

diffusion safety

arXiv AI

Jun 24

Ensemble Distributionally Robust Bayesian Optimisation with Continuous Context

arXiv:2605. 07565v2 Announce Type: replace-cross Abstract: We study Bayesian Optimisation (BO) in settings where the objective function is influenced by uncontrollable environmental contexts governed by an unknown probability distribution.

By Tigran Ramazyan, Denis Derkach

benchmarks

arXiv AI

Jun 16

MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance

arXiv:2606. 16923v1 Announce Type: new Abstract: Simulation-based inference (SBI) of latent parameters is often hindered by simulator misspecification, the mismatch between simulated and real-world observations caused by inherent modeling simplifications.

By Arunkumar V, Manoranjan Gandhudi, Gangadharan G. R., Arun Prakash, S. Senthilkumar

benchmarks safety

arXiv Machine Learning

Jun 2

Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing

arXiv:2604. 17838v2 Announce Type: replace Abstract: Generative modeling within constrained sets is essential for scientific and engineering applications involving physical, geometric, or safety requirements (e.

By Kijung Jeon, Michael Muehlebach, Molei Tao

diffusion robotics benchmarks safety

arXiv AI

Jul 1

Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems

arXiv:2508. 09156v3 Announce Type: replace-cross Abstract: We present a framework for fine-tuning flow-matching generative models to enforce physical constraints and solve inverse problems in scientific systems.

By Jan Tauberschmidt, Sophie Fellenz, Sebastian J. Vollmer, Andrew B. Duncan

fine-tuning benchmarks

arXiv Machine Learning

22h ago

Composing Flow-Matching Energies with Known Physics: Generation, OOD Detection, and Inversion on PDE Fields

arXiv:2608. 18004v1 Announce Type: new Abstract: Probabilistic modeling of physical fields benefits from both a data-driven prior and known physical structure such as the governing equations.

By Yixuan Sun, Anirban Samaddar, Sandeep Madireddy

diffusion

arXiv Machine Learning

Jun 11

Structure-Preserving Neural Surrogates with Tractable Uncertainty Quantification

arXiv:2606. 11650v1 Announce Type: new Abstract: Recent advances in scientific machine learning provide a means of near-real-time solution to partial differential equations (PDEs), but lack the theoretical underpinnings of conventional simulators that support contemporary verification and validation.

By Handi Zhang, Adrienne M. Propp, Brooks Kinch, Houman Owhadi, Nathaniel Trask

llms

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