The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: AI4S: 6th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications
Authors: Leslie Horace (Georgia Institute of Technology), Christin Whitton and Vanessa Job (Los Alamos National Laboratory (LANL)), William Jones (Coastal Carolina University), and Nathan DeBardeleben (Los Alamos National Laboratory (LANL))
Abstract: We propose a conditional normalizing flow (CNF) surrogate model to solve generative, many-to-one inverse problems in scientific simulations governed by partial differential equations (PDEs) with time-evolving interactions between heterogeneous materials. We present two case studies: electrostatic potential and heat diffusion, which serve as proxy simulations for generating diverse sets of initial conditions that can reproduce an observed output state (transient or steady). Finally, we provide a comprehensive overview of the synthetic datasets, the model specification, each stage of the experimental workflow, evaluation of training performance, and uncertainty quantification for the generated samples.
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