About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Accelerating Phase-Field Predictions Via History-Dependent Neural Networks Learning Microstructural Evolution in a Latent Space |
Author(s) |
Remi Dingreville, Chongze Hu, Shawn Martin |
On-Site Speaker (Planned) |
Remi Dingreville |
Abstract Scope |
The phase-field method is a powerful and versatile computational approach for modeling the evolution of the microstructure and properties of a wide variety of physical, chemical and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve useful degree of accuracy. In this talk I will discuss advanced in developing computationally inexpensive and accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine learning techniques. I discuss the advantages/disadvantages of combining various techniques to integrate low-dimensional description of the microstructure, obtained directly from phase-field simulations, with history-dependent deep neural network. Lasty, I will give examples on the performance and accuracy of the established machine-learning accelerated framework to predict the non-linear microstructure evolution as compared to high-fidelity phase-field. |
Proceedings Inclusion? |
Undecided |