arXiv Machine Learning

BubbleSH: A Dataset of Rising Bubbles with Deformable Interfaces

arXiv:2607. 07275v1 Announce Type: new Abstract: Bubbly flows exhibit complex multiscale dynamics, with deformable bubbles interacting through the surrounding liquid and giving rise to strongly coupled kinematic and morphological behavior.

Hugging Face Trending Papers
Jul 8

BubbleSH: A Dataset of Rising Bubbles with Deformable Interfaces

Bubbly flows exhibit complex multiscale dynamics, with deformable bubbles interacting through the surrounding liquid and giving rise to strongly coupled kinematic and morphological behavior. We present BubbleSH, a bubbly flows dataset consisting of transient, three-dimensional bubble-swarm dynamics obtained from high-fidelity direct numerical simulations of bubbles rising in a periodic domain.

arXiv Machine Learning
Jul 13

Transformer-Based Inverse Microrheology for Experimental Mechanics at Ultra-High Strain Rates

arXiv:2506. 11936v2 Announce Type: replace-cross Abstract: Traditional rheological tools are often limited in characterizing soft materials under ultra-high strain-rate loading conditions (> 1000 s^-1) due to constraints in spatiotemporal resolution, loading rate, and invasiveness.

By Lehu Bu, Zhaohan Yu, Danila Frolkin, Junyoung Kim, Qihang Shi, Jan N. Fuhg, Shaoting Lin, Jin Yang
arXiv Machine Learning
Aug 12

HyperShape: Hyperelasticity Across Diverse Shapes

arXiv:2608. 09938v1 Announce Type: cross Abstract: Hyperelastic deformations are highly sensitive to domain geometry and boundary conditions, making generalization across both a critical capability for neural operators applied to these problems.

By Leo Widmer, Sidaty El Hadramy, St\'ephane Cotin, Philippe Claude Cattin
arXiv AI
Jul 28

Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows

arXiv:2607. 22280v2 Announce Type: replace-cross Abstract: Compressible multiphase flows involving shocks and material interfaces arise in applications such as bubble collapse and droplet breakup, where strong nonlinear interactions produce complex interface deformation, mixing, and multiscale dynamics.

By Harish Ramachandran, Bj\"orn Kimpel, Thomas Paula, Josef Winter, Steffen Schmidt, Nikolaus Adams
arXiv AI
Jul 2

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction

arXiv:2607. 00460v1 Announce Type: cross Abstract: Predicting complex spatiotemporal dynamics in physical processes often demands computationally expensive numerical methods or data-driven neural networks that suffer from high training costs, error accumulation, and limited generalizability to unseen parameters.

By Xin-Yang Liu, Xiantao Fan, Jian-Xun Wang
arXiv AI
Jul 1

The HydroGym Reinforcement Learning Platform for Fluid Dynamics

arXiv:2512. 17534v2 Announce Type: replace-cross Abstract: Modeling and controlling fluids is critical across science and engineering.

By Christian Lagemann, Sajeda Mokbel, Miro Gondrum, Mario R\"uttgers, Yuning Wang, Pol Su\'arez, Ludger Paehler, Deniz A. Bezgin, Aaron B. Buhendwa, Jared L. Callaham, Samuel Ahnert, Nicholas Zolman, Xiao Shao, Jean-Christophe Loiseau, Nikolaus Adams, Matthias Meinke, Wolfgang Schr\"oder, Kai Lagemann, Esther Lagemann, Ricardo Vinuesa, Steven L. Brunton