arXiv Machine Learning By J. Storm, I. B. C. M. Rocha, S. Schyck, K. Masania, F. P. van der Meer

Microstructure-Conditioned Surrogate Models for Graded Multiscale Optimization of Mycelium Composites

Read the original on arXiv Machine Learning →

arXiv:2607. 13688v1 Announce Type: new Abstract: Emerging sustainable materials increasingly rely on engineered hierarchy and microstructure to achieve control of their properties and mechanical behavior.

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arXiv Machine Learning
Jun 30

On Surrogate Modeling of Static Response of AM Short-Fiber Thermoplastics Using Graph Neural Networks

arXiv:2606. 28996v1 Announce Type: new Abstract: Short-fiber thermoplastic (SFT) composites are increasingly employed in lightweight aerospace and automotive structures owing to their favorable strength-to-weight ratio, high production rates, and recyclability.

By Pharindra Pathak (Auburn University, Oakridge National Lab, NASA Glenn Research Center, Auburn University, Auburn University), Vipin Kumar (Auburn University, Oakridge National Lab, NASA Glenn Research Center, Auburn University, Auburn University), Trenton M. Ricks (Auburn University, Oakridge National Lab, NASA Glenn Research Center, Auburn University, Auburn University), Suhasini Gururaja (Auburn University, Oakridge National Lab, NASA Glenn Research Center, Auburn University, Auburn University), Siddhartha Srivastava (Auburn University, Oakridge National Lab, NASA Glenn Research Center, Auburn University, Auburn University)
arXiv Machine Learning
Jun 10

Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks

arXiv:2606. 10909v1 Announce Type: cross Abstract: Reconstructing local stress fields in heterogeneous microstructures under non-linear, history-dependent loading remains a major computational bottleneck in multi-scale simulations.

By Manuel Ricardo Guevara Garban, Yves Chemisky, \'Etienne Pruli\`ere, Micha\"el Cl\'ement, Martin Abendroth, Bj\"orn Kiefer
arXiv Machine Learning
Jun 30

Implementation of Hyperelastic Physics-Augmented Neural Networks in the Explicit Finite Element Codes Simcenter Radioss and OpenRadioss with Applications to Impact Events

arXiv:2606. 29874v1 Announce Type: cross Abstract: Data-driven material modeling techniques have gained significant attention due to their ability to capture complex constitutive behaviors beyond the limitations of classical material models.

By Lukas Maurer, Sascha Eisentr\"ager, Marian Bulla, Daniel Juhre
arXiv Machine Learning
Jun 19

A Hybrid GNN-FEM Framework for Phase-Field Fracture Simulation. Physics-Preserving Hybridization for Generalizable Surrogate Modeling

arXiv:2606. 19378v1 Announce Type: new Abstract: Scientific machine learning (SciML) has emerged as a promising approach for accelerating simulations of complex physical systems, yet achieving physically consistent and generalizable predictions for nonlinear, history-dependent problems remains a central challenge.

By Hyeonbin Moon, Yongjin Choi, Seunghwa Ryu