arXiv AI August 3, 2026 By Anuraj Maurya

Patch-Based 3D Variational Autoencoder for Super-Resolution of Turbulent Channel Flow

arXiv:2507. 22082v2 Announce Type: replace-cross Abstract: Direct numerical simulation (DNS) accurately resolves all spatio-temporal scales of wall-bounded turbulence but becomes prohibitively expensive as the Reynolds number increases.

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 AI.

diffusion

arXiv Machine Learning

Jun 30

Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence

arXiv:2507. 00719v3 Announce Type: replace-cross Abstract: Typically, numerical simulations of Earth systems are coarse, and Earth observations are sparse and gappy.

By Anantha Narayanan Suresh Babu, Akhil Sadam, Pierre F. J. Lermusiaux

diffusion

arXiv Machine Learning

Jun 11

Spectrally Regularized Latent Flow Matching for Turbulence Generation

arXiv:2606. 11691v1 Announce Type: new Abstract: Latent diffusion and flow matching have emerged as leading approaches for synthetic turbulence generation, yet they systematically under-represent dissipation-range amplitudes.

By Khalid Rafiq, Aditya G. Nair

diffusion safety

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

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

benchmarks

arXiv Machine Learning

2d ago

Shaping the Wind: Nested Potentials for Kinematically Admissible Urban Wind Prediction

arXiv:2610.07033v1 Announce Type: new Abstract: Predicting transient urban winds is fundamental to understanding urban microclimates and designing climate-resilient cities. Building-resolving large-e...

By Yidi Wang, Yunhe Zhang, Jiawei Gu, Ziyue Qiao, Pengyang Wang

arXiv Machine Learning

Aug 18

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

arXiv:2608. 14744v1 Announce Type: new Abstract: Recovering high-resolution states from sparse, low-resolution observations is a central challenge in scientific machine learning and data assimilation.

By Mrigank Dhingra, Ramchandran Muthukumar, Rebecca Willett, Omer San

diffusion benchmarks

One email a morning, machine-written

Related stories

Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence

Spectrally Regularized Latent Flow Matching for Turbulence Generation

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

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

Shaping the Wind: Nested Potentials for Kinematically Admissible Urban Wind Prediction

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