arXiv Machine Learning

Prediction of Viscoelastic Droplet Impact Dynamics Using a Vision Transformer-Based Approach

arXiv:2606. 23940v1 Announce Type: cross Abstract: Droplet impact on solid surfaces is a complex fluid dynamics problem with applications in spray cooling, inkjet printing, and pharmaceutical processing.

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 AI
Jun 16

Learning Interface Breakup: A Geometry-Conditioned Latent Surrogate for Spray Formation

arXiv:2606. 16587v1 Announce Type: cross Abstract: Designing spray nozzles requires predicting how geometry shapes transient two-phase breakup, but high-fidelity volume-of-fluid (VOF) simulations with adaptive mesh refinement (AMR) are too expensive for iterative design exploration.

By Julius H Ramlau, Friedrich Hastedt, Tolga Birdal, Ehecatl-Antonio del R\'io Chanona, Nausheen S Basha, Omar K Matar
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 Machine Learning
Jun 2

MPMWorlds: Material-Point-Method Simulations for Inferring and Extrapolating Physical Dynamics

arXiv:2606. 01538v1 Announce Type: cross Abstract: To study the ability to infer physical dynamics from videos and extrapolate them forward in time, we assemble a dataset of 2D Material Point Method (MPM) physical simulations covering rich physical phenomena such as deformable objects, fluids, kinetic objects, and emitters.

By \v{Z}iga Kova\v{c}i\v{c}, Kevin Ellis
arXiv AI
Jul 29

Physics-Grounded Fluid Video Generation with a Simulation Dataset and Dual-Stream Optical-Flow Supervision

arXiv:2607. 25321v1 Announce Type: new Abstract: Video diffusion models generate visually compelling content but routinely violate elementary physics when the subject involves fluids: liquid columns break apart in mid-air, container water levels fail to rise as liquid is poured in, and splashes disperse without regard to momentum or gravity.

By Ruijie Su, Yuanzhi Liang, Xiaohua Xie, Jianhuang Lai
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
Jun 25

Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach

arXiv:2602. 22188v2 Announce Type: replace Abstract: Modelling rock-fluid interaction requires solving a set of partial differential equations (PDEs) to predict the flow behaviour and the reactions of the fluid with the rock on the interfaces.

By Nathalie C. Pinheiro, Donghu Guo, Hannah P. Menke, Aniket C. Joshi, Claire E. Heaney, Ahmed H. ElSheikh, Christopher C. Pain