arXiv AI August 18, 2026 By Chang Liu, Bohao Zhao, Jingtao Ding, Huandong Wang, Yong Li

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

arXiv:2505. 23863v3 Announce Type: replace-cross Abstract: Understanding chaotic dynamics is a fundamental problem across scientific disciplines, including climate science, neuroscience, and fluid dynamics, yet direct experimentation and intervention in such systems are often infeasible.

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

arXiv Machine Learning

Jul 17

A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series

arXiv:2511. 06609v4 Announce Type: replace Abstract: The accurate forecasting of complex, high-dimensional dynamical systems from observational data is a fundamental task across numerous scientific and engineering disciplines.

By Xuyang Li, John Harlim, Dibyajyoti Chakraborty, Romit Maulik

benchmarks

arXiv Machine Learning

Jul 3

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.

By Zhangyong Liang, Huanhuan Gao

rag benchmarks

arXiv AI

Aug 5

A neural operator framework for data-driven discovery of stability and receptivity in physical systems

arXiv:2604. 19465v3 Announce Type: replace-cross Abstract: Understanding how complex systems respond to perturbations, such as whether they will remain stable or what their most sensitive patterns are, is a fundamental challenge across science and engineering.

By Chengyun Wang, Liwei Chen, Nils Thuerey

arXiv AI

Jul 7

When is a System Discoverable from Data? Discovery Requires Chaos

arXiv:2511. 08860v2 Announce Type: replace-cross Abstract: The deep learning revolution has spurred a rise in advances of using AI in sciences.

By Zakhar Shumaylov, Peter Zaika, Philipp Scholl, Gitta Kutyniok, Lior Horesh, Carola-Bibiane Sch\"onlieb

benchmarks

arXiv Machine Learning

Jun 5

Uncovering Extreme Event Mechanisms for Prediction and Control with Sensitivity-Balanced Projections

arXiv:2606. 05618v1 Announce Type: cross Abstract: Extreme events -- such as earthquakes and coronal mass ejections -- are common in many chaotic dynamical systems, yet are difficult to characterize and predict due to the subtle instability mechanisms that drive them.

By Nicholas Zolman, Sajeda Mokbel, Samuel E. Otto, Steven L. Brunton

arXiv Machine Learning

Jun 8

Unified Geometry-Guided ML-FTLE for Tracking Transient Chaos from Scalar Time Series

arXiv:2606. 07385v1 Announce Type: cross Abstract: Detecting transient chaos from scalar observations without governing equations represents a fundamental challenge in nonlinear dynamics.

By S. V. Manivelan, Andrei Velichko, I. Manimehan

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A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series

Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs

A neural operator framework for data-driven discovery of stability and receptivity in physical systems

When is a System Discoverable from Data? Discovery Requires Chaos

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