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USACM Nanotechnology Virtual Seminar
Wednesday, August 31, 2022, 2:00 PM - 3:00 PM CST
Category: Events

USACM Nanotechnology Virtual Seminar

The Intersection of Machine Learning & Uncertainty Quantification in Physics-Based Modeling for Materials Systems


Michael Shields, Johns Hopkins University


Anter El-Azab, Purdue University



Machine Learning (ML) and Uncertainty Quantification (UQ) have gained widespread popularity in the scientific community, to such an extent that ML/UQ seems to appear in some capacity in nearly all modern scientific investigations. This is particularly true in physics-based modeling where machine learning algorithms are being specially designed to adhere to physical principles, such as the popular physics-informed neural networks (PINNs). In this talk, we will discuss the relationship between these two important research areas in the context of physics-based modeling, with an emphasis on simulating materials systems. We will specifically discuss how the two areas complement one another to enhance modeling capability by making the critical distinction between UQ for ML and ML for UQ. In the former case, we will argue that modern ML methods require UQ as an integral component and show recent advances in the learning of uncertainty-aware Bayesian Neural Networks. In the latter case, we will argue that UQ can be viewed as an exercise in ML and will show how modern ML methods (from Hamiltonian Neural Networks to manifold learning) enable UQ in physical systems while, in many cases, remaining constrained by the underlying physics. Applications to materials modeling will be shown ranging from equation of state modeling for warm dense matter to hierarchical multi-scale models for structural mechanics.