Digitalizing metallic materials from image segmentation to multiscale solutions via physics informed operator learning

Digitalizing metallic materials from image segmentation to multiscale solutions via physics informed operator learning

Abstract Fast prediction of microstructural responses based on realistic material topology is vital for linking process, structure, and properties. This work presents a digital framework for metallic materials using microscale features. We explore deep learning for two primary goals: (1) segmenting...

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Main Authors: Shahed Rezaei, Kianoosh Taghikhani, Alexandre Viardin, Reza Najian Asl, Ali Harandi, Nikhil Vijay Jagtap, David Bailly, Hannah Naber, Alexander Gramlich, Tim Brepols, Mustapha Abouridouane, Ulrich Krupp, Thomas Bergs, Markus Apel
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:npj Computational Materials
Online Access:https://doi.org/10.1038/s41524-025-01718-y
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https://doi.org/10.1038/s41524-025-01718-y

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