arXiv Machine Learning August 11, 2026 By Ali SaraerToosi, Renbo Tu, Esther Y. H. Lin, Kamyar Azizzadenesheli, Aviad Levis

NeuralDMD: Interpretable Neural Representation of Dynamics from Sparse and Noisy Measurements

Read the original on arXiv Machine Learning →

arXiv:2507. 03094v2 Announce Type: replace-cross Abstract: Many challenges in scientific imaging involve solving ill-posed inverse problems, where the goal is to recover spatio-temporal fields from indirect, noisy, and highly sparse measurements - often without access to ground truth data or reliable simulators.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

efficiency

arXiv Machine Learning

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Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs

arXiv:2603. 12676v3 Announce Type: replace Abstract: Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harder and optimization less stable.

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rag benchmarks

arXiv Machine Learning

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Iterative Refinement Diffusion for Super-Resolved Data Assimilation of Multiscale Physical Systems

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diffusion benchmarks

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PhyxMamba: Chaotic System Reconstruction from Short Context Observations with Generative State-Space Models

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By Chang Liu, Bohao Zhao, Jingtao Ding, Huandong Wang, Yong Li

rag

arXiv AI

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Physics-Encoded Inverse Modeling for Arctic Snow Depth Prediction

arXiv:2601. 17074v4 Announce Type: replace-cross Abstract: Accurate estimation in time-varying inverse problems under limited and sparse observations remains a fundamental challenge across scientific domains.

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computer-vision

arXiv AI

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Physics-Encoded Inverse Modeling for Arctic Snow Depth Estimation

arXiv:2601. 17074v5 Announce Type: replace-cross Abstract: Accurate estimation of unobserved quantities in time-varying inverse problems remains challenging when observations are sparse and only indirectly related to the target variable.

By Akila Sampath, Vandana P. Janeja, Jianwu Wang

computer-vision

arXiv Machine Learning

Jun 9

ForcingDAS: Unified and Robust Data Assimilation via Diffusion Forcing

arXiv:2605. 14285v2 Announce Type: replace-cross Abstract: Data assimilation (DA) estimates the state of an evolving dynamical system from noisy, partial observations, and is widely used in scientific simulation as well as weather and climate science.

By Yixuan Jia, Siyi Chen, Yida Pan, Xiao Li, Lianghe Shi, Chanyong Jung, Haijie Yuan, Ismail Alkhouri, Yue Cynthia Wu, Saiprasad Ravishankar, Jeffrey A Fessler, Qing Qu

diffusion benchmarks

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Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs

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