Predictive Scale-Bridging Simulations through Active Learning
This addresses the computational bottleneck of scale-bridging for scientists in fields like energy extraction and fusion research, representing a novel method rather than an incremental improvement.
The paper tackles the problem of achieving greater physical fidelity in computational simulations through scale-bridging, developing an active learning approach that optimizes the use of fine-scale simulations to inform coarse-scale hydrodynamics, with applications in nanoporous media transport and inertial confinement fusion.
Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models.