arXiv Machine Learning June 30, 2026 By Samuel Ahnert, Esther Lagemann, H. Jane Bae, Kunihiko Taira, Ricardo Vinuesa, Christian Lagemann, Steven L. Brunton

Weak Dominant Balance for Robust Identification of Dynamically Consistent Fluid Flow Structure

Read the original on arXiv Machine Learning →

arXiv:2606. 29047v1 Announce Type: cross Abstract: Extracting interpretable, localized physical mechanisms from complex spatiotemporal data is a foundational challenge across physics, biology, and engineering, but has remained out of reach on real measurements.

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

arXiv Machine Learning

Jun 11

Deep Learning of Solver-Aware Turbulence Closures from Nudged LES Dynamics

arXiv:2604. 23874v3 Announce Type: replace-cross Abstract: The differentiable physics paradigm may be leveraged as an a-posteriori approach for discovering turbulence closure models by embedding a neural network parameterization directly inside the solver and optimizing it given potentially sparse target data.

By Ashwin Suriyanarayanan, Dibyajyoti Chakraborty, Romit Maulik

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arXiv Machine Learning

Jul 15

A Shortcut to Statistically Steady-State Turbulence with Flow Matching

arXiv:2607. 13022v1 Announce Type: cross Abstract: Many nonlinear physical systems exhibit an initial transient phase in which perturbations grow before nonlinear interactions lead to a statistically steady state.

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arXiv Machine Learning

Jun 5

Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks

arXiv:2606. 06313v1 Announce Type: cross Abstract: Wall shear stress (WSS) governs near-wall transport dynamics and is a key hemodynamic indicator in cardiovascular flows, yet remains difficult to infer accurately due to the need for precise computation of near-wall velocity gradients.

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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.

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arXiv Machine Learning

Jun 19

Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport

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By Gabriel F. Barros, R\^omulo M. Silva, Alvaro L. G. A. Coutinho

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arXiv Machine Learning

Aug 6

TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning

arXiv:2608. 04222v1 Announce Type: cross Abstract: Turbulence is a central testbed for machine learning on physical dynamics because its governing laws are known exactly.

By Yilong Dai, Yiming Sun, Yiheng Chen, Shengyu Chen, Peyman Givi, Xiaowei Jia, Runlong Yu

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