Presentation
Inverse Design for Generating Initial Conditions in Scientific Simulations
SessionAI4S: 6th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications
DescriptionWe 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.
Event Type
Workshop
TimeMonday, 17 November 20254:30pm - 4:50pm CST
Location274