Bibliographic Details
| Title: |
Rethinking materials simulations: Blending direct numerical simulations with neural operators |
| Authors: |
Oommen, Vivek, Shukla, Khemraj, Desai, Saaketh, Dingreville, Remi, Karniadakis, George Em |
| Publication Year: |
2023 |
| Collection: |
Computer Science
Physics (Other) |
| Subject Terms: |
Computer Science - Machine Learning, Physics - Computational Physics |
| More Details: |
Direct numerical simulations (DNS) are accurate but computationally expensive for predicting materials evolution across timescales, due to the complexity of the underlying evolution equations, the nature of multiscale spatio-temporal interactions, and the need to reach long-time integration. We develop a new method that blends numerical solvers with neural operators to accelerate such simulations. This methodology is based on the integration of a community numerical solver with a U-Net neural operator, enhanced by a temporal-conditioning mechanism that enables accurate extrapolation and efficient time-to-solution predictions of the dynamics. We demonstrate the effectiveness of this framework on simulations of microstructure evolution during physical vapor deposition modeled via the phase-field method. Such simulations exhibit high spatial gradients due to the co-evolution of different material phases with simultaneous slow and fast materials dynamics. We establish accurate extrapolation of the coupled solver with up to 16.5$\times$ speed-up compared to DNS. This methodology is generalizable to a broad range of evolutionary models, from solid mechanics, to fluid dynamics, geophysics, climate, and more. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2312.05410 |
| Accession Number: |
edsarx.2312.05410 |
| Database: |
arXiv |
