Applied & Computational Mathematics seminar: Integrating Machine-Learned Surrogate Models with Simulations

Speaker:  Bethany Lusch from Argonne National Laboratory

Title:  Integrating Machine-Learned Surrogate Models with Simulations

Abstract:  Simulations can be computationally expensive, so it can be advantageous to use machine learning to train a surrogate model that is orders of magnitude faster. However, completely data-driven black-box models often have disadvantages such as limited generalizability and the chance of physically-impossible predictions. I will describe our recent work on surrogate modeling for applications such as automotive engines and weather, as well as how we are creating hybrid models by integrating surrogate models back into simulations.
 
Time:  Friday, April 22, 2022, 10:00am-11:00am
Place:  Zoom