How to Enable Complex Simulations: The Power of Multiphysics & Digital Thread Seminar

The Vital Role of Machine Learning in Enhancing Simulations

Author: Peter Chien - University of Wisconsin-Madison

Abstract

Simulations and digital twins stand as pivotal components within the realm of digital thread and engineering. However, simulations and digital twins pose several challenges:

Resource-Intensive Nature: The execution of simulations and digital twins is often time- consuming. This presents a significant obstacle when attempting to undertake tasks heavily reliant on sampling.
Discrepancies with Test Data: Disparities frequently arise between simulation outcomes and test or physical data.
Ubiquitous Uncertainties: Simulations and digital twins are subject to a multitude of uncertainties, stemming from sources like boundary and initial conditions and geometric variations.

Addressing these challenges necessitates the integration of modern machine learning techniques. In this talk, I will delve into the application of contemporary machine learning methodologies to solve these problems. The presentation will be structured around the following focal points:

Advanced design of experiments, including Sudoku.
Rapid predictive modeling
Calibration techniques to narrow the disparities between simulation results and test data.

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