Learning two-phase microstructure evolution using neural operators and autoencoder architectures

Oommen, Vivek; Shukla, Khemraj; Goswami, Somdatta; Dingreville, Rémi; Karniadakis, George Em

doi:10.1038/s41524-022-00876-7

Learning two-phase microstructure evolution using neural operators and autoencoder architectures

Journal Article · Fri Sep 02 00:00:00 EDT 2022 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-022-00876-7· OSTI ID:1885197

; Shukla, Khemraj; ; ;

Abstract

Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and generalizable surrogate models are needed to alleviate the cost of computationally taxing processes such as in optimization and design of materials. The intrinsic discontinuous nature of the physical phenomena incurred by the presence of sharp phase boundaries makes the training of the surrogate model cumbersome. We develop a framework that integrates a convolutional autoencoder architecture with a deep neural operator (DeepONet) to learn the dynamic evolution of a two-phase mixture and accelerate time-to-solution in predicting the microstructure evolution. We utilize the convolutional autoencoder to provide a compact representation of the microstructure data in a low-dimensional latent space. After DeepONet is trained in the latent space, it can be used to replace the high-fidelity phase-field numerical solver in interpolation tasks or to accelerate the numerical solver in extrapolation tasks.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: NA0003525

OSTI ID:: 1885197

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 8; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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