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 Computer Vision
Sep 18

SplashSplat: Reconstructing Splashing Liquids from Real-World Multi-View Videos

SplashSplat introduces a new benchmark of 20 real-world splashing liquid scenes captured with seven synchronized 4K cameras at 60 fps, providing per-view liquid and container masks and fixed evaluation splits. The method reconstructs per‑frame liquid signed distance fields (SDFs) from these masks, fuses them into a coarse velocity field, and uses Lagrangian carriers to generate differentiable local Gaussian representations for rendering. SplashSplat outperforms existing dynamic Gaussian splatting techniques on both real and synthetic data, offering more physically plausible motion, lower training cost, and enabling temporal interpolation and style transfer without re‑optimization.

By Peiyu Liu, Dingxi Zhang, Federico Tombari, Marc Pollefeys, Christina Tsalicoglou, Daniel Barath
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
Sep 24

Learning Stiffness Dependent Fluid Structure Dynamics from Coarse Flow Representations

The paper presents a data‑driven framework that predicts long‑term fluid–structure interaction dynamics for a flexible plate undergoing flow‑induced vibration. It uses a stiffness‑conditioned neural evolution operator that jointly models the Eulerian flow field and the Lagrangian structural state, employing a hybrid CNN‑Transformer architecture with bidirectional cross‑attention. The operator accurately captures three stiffness‑dependent response regimes, preserves key flow and structural features over 1000‑step rollouts, and can interpolate to unseen stiffness values, while a differentiable aerodynamic‑force module based on derivative‑moment transformation enables accurate lift and drag reconstruction.

By Chun-Jun Pu, Li-Wei Chen, Hai-Bo Huang
arXiv Computer Vision
4d ago

FracGen: Learning How Objects Stretch and Tear with Physics-Informed Video Generation

FracGen is a fracture‑aware video generation model that creates realistic, controllable fracture dynamics from a single intact image, guided by physics signals. It is trained using FracSim, a simulation framework that extends material point method (MPM) with a continuum damage model to produce paired fracture videos and dense physical fields. The model jointly predicts RGB video and physical maps, employing physics‑informed losses to capture material‑specific fracture behavior and enabling fine‑grained control over tear location, crack speed, and deformation before failure.

By Trong-Tung Nguyen, Jiahan Zhang, Anand Bhattad
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