Geometry-aware framework for deep energy method: an application to structural mechanics with hyperelastic materials
Physics-Informed Neural Networks (PINNs) have gained considerable interest in diverse
engineering domains thanks to their capacity to integrate physical laws into deep learning …
engineering domains thanks to their capacity to integrate physical laws into deep learning …
HyperSBINN: A Hypernetwork-Enhanced Systems Biology-Informed Neural Network for Efficient Drug Cardiosafety Assessment
I Soukarieh, G Hessler, H Minoux, M Mohr… - arXiv preprint arXiv …, 2024 - arxiv.org
Mathematical modeling in systems toxicology enables a comprehensive understanding of
the effects of pharmaceutical substances on cardiac health. However, the complexity of …
the effects of pharmaceutical substances on cardiac health. However, the complexity of …
[PDF][PDF] Separable Physics-Informed Neural Networks for Robust Inverse Quantification in Solid Mechanics
Over the last decade, the emergence of full-field measurement techniques, such as digital
image correlation (DIC), has revolutionized material testing. These advancements usher in a …
image correlation (DIC), has revolutionized material testing. These advancements usher in a …
GADEM: a geometry-aware energy-based method for structural mechanics problems
Physics-Informed Neural Networks (PINNs) have gained significant attention in various
engineering domains thanks to their capacity to incorporate physical laws into models …
engineering domains thanks to their capacity to incorporate physical laws into models …