← Back to all news
arXiv Machine Learning 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

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
rag
More like this →
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.

By Gianluca Galletti, Gerald Gutenbrunner, William Hornsby, Lorenzo Zanisi, Naomi Carey, Stanislas Pamela, Johannes Brandstetter, Fabian Paischer
diffusion
More like this →
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.

By Mahmoud Elhadidy, Siva Viknesh, Roshan M. D'Souza, Amirhossein Arzani
benchmarks
More like this →