Scale bridging materials physics: active learning workflows and integrable deep neural networks for free energy function representations in alloys

DOI10.1016/j.cma.2020.113281zbMath1506.74241arXiv2002.02305OpenAlexW3080892009MaRDI QIDQ2021083

Gregory H. Teichert, Krishna Garikipati, A. R. Natarajan, Ad H. G. S. van der Ven

Publication date: 26 April 2021

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2002.02305

zbMATH Keywords

continuum mechanicsstatistical mechanicsmachine learningphase field modelingfirst-principles calculations

Mathematics Subject Classification ID

Neural nets applied to problems in time-dependent statistical mechanics (82C32) Analysis of microstructure in solids (74N15)

Related Items (1)

A heteroencoder architecture for prediction of failure locations in porous metals using variational inference

Uses Software

• TOMS659
• ImageNet
• GitHub
• AlexNet

Cites Work

• Unnamed Item
• Machine learning materials physics: surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics
• Machine learning materials physics: integrable deep neural networks enable scale bridging by learning free energy functions
• A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality
• The deal.II library, version 8.5
• Active Learning
• Algorithm 659
• Anisotropy of interfaces in an ordered alloy: a multiple–order–parameter model
• Isogeometric Analysis
• Free Energy of a Nonuniform System. I. Interfacial Free Energy
• On the distribution of points in a cube and the approximate evaluation of integrals

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