arXiv Machine Learning July 13, 2026 By Lehu Bu, Zhaohan Yu, Danila Frolkin, Junyoung Kim, Qihang Shi, Jan N. Fuhg, Shaoting Lin, Jin Yang

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv AI

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Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models

arXiv:2602. 17750v2 Announce Type: replace-cross Abstract: A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation between strain history and stress.

By Chenyi Ji, Kian P. Abdolazizi, Hagen Holthusen, Christian J. Cyron, Kevin Linka

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arXiv Machine Learning

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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.

By Rachna Ramesh, Kiet Bennema ten Brinke, Douwe Orij, Ivo Roghair, Vlado Menkovski

benchmarks

arXiv Machine Learning

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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.

By Diego A. de Aguiar, Cassio M. Oishi

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Hugging Face Trending Papers

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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.

benchmarks

arXiv Machine Learning

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Probabilistic Physics-Informed Neural Networks for Estimating Heterogeneous Elastic Properties from Low-Resolution and Noisy Displacement Data

arXiv:2607. 14563v1 Announce Type: new Abstract: Estimating spatially heterogeneous elastic properties from low-resolution displacement measurements is a severely ill-posed inverse elasticity problem because low resolution obscures spatial details needed to distinguish heterogeneous property variations, and small measurement perturbations or fitting errors are amplified through inverse estimation.

By Tatthapong Srikitrungruang, Jaesung Lee

arXiv AI

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A Convex Route to Thermoelasticity: Learning Internal Energy and Dissipation

arXiv:2603. 28707v3 Announce Type: replace-cross Abstract: We present a physics-based neural network framework for the discovery of constitutive models in fully coupled thermomechanics.

By Hagen Holthusen, Paul Steinmann, Ellen Kuhl

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Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models

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BubbleSH: A Dataset of Rising Bubbles with Deformable Interfaces

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