2024-08-28 サンディア国立研究所(SNL)
＜関連情報＞
- https://newsreleases.sandia.gov/swifter_simulations/
- https://www.nature.com/articles/s41524-024-01319-1
材料シミュレーションの再考：直接数値シミュレーションとニューラル・オペレータの融合 Rethinking materials simulations: Blending direct numerical simulations with neural operators
Vivek Oommen,Khemraj Shukla,Saaketh Desai,Rémi Dingreville & George Em Karniadakis
npj Computational Materials Published:04 July 2024
DOI:https://doi.org/10.1038/s41524-024-01319-1
Abstract
Materials simulations based on direct numerical solvers are accurate but computationally expensive for predicting materials evolution across length- and time-scales, due to the complexity of the underlying evolution equations, the nature of multiscale spatiotemporal interactions, and the need to reach long-time integration. We develop a method that blends direct 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 to enable accurate extrapolation and efficient time-to-solution predictions of the dynamics. We demonstrate the effectiveness of this hybrid framework on simulations of microstructure evolution via the phase-field method. Such simulations exhibit high spatial gradients and the co-evolution of different material phases with simultaneous slow and fast materials dynamics. We establish accurate extrapolation of the coupled solver with large speed-up compared to DNS depending on the hybrid strategy utilized. This methodology is generalizable to a broad range of materials simulations, from solid mechanics to fluid dynamics, geophysics, climate, and more.
