Document Type
Article
Publication Date
11-15-2025
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Leslie Horace, Christin Whitton, Vanessa Job, William Jones, and Nathan DeBardeleben. 2025. Inverse Design for Generating Initial Conditions in Scientific Simulations. In Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC Workshops ’25), November 16–21, 2025, St Louis, MO, USA. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3731599.3767343. Available at https://digitalcommons.coastal.edu/computing/1/