JAX-FEM: A differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data science

This paper introduces JAX-FEM, an open-source differentiable finite element method (FEM) library. Constructed on top of Google JAX, a rising machine learning library focusing on high-performance numerical computing, JAX-FEM is implemented with pure Python while scalable to efficiently solve problems...

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Bibliographische Detailangaben

1. Verfasser:	John Ballantyne
Format:	Dataset
Sprache:	eng
Online-Zugang:	Volltext bestellen
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