CAM Colloquium-- Danial Faghihi, Mechanical & Aerospace Engineering, University at Buffalo

Description

Title: Strategies for Selecting Optimal Predictive Computational Models in the Face of Uncertainty

Abstract: Recent advances in computational science have revolutionized the utilization of large-scale data from images and simulations to significantly enhance predictions of complex physical systems. This hinges on the incorporation of physics-based and scientific machine learning surrogate models for high-consequence decision-making. At the core of scientific prediction lies the formidable challenge of model validation in the face of uncertainties, encompassing data limitations, modeling errors, and, most crucially, selecting the model formulation itself. This talk introduces computational frameworks founded on the Occam-Plausibility Algorithm (OPAL) for selecting an "optimal" predictive model from a multitude of potential physics-based and neural network surrogate models, each differing in fidelity and complexity. OPAL harnesses Bayesian inference for model calibration and validation, along with the notion of model plausibility, to systematically and efficiently identify the simplest valid model that delivers sufficiently accurate computational predictions. The key feature to ensure the predictive ability of the model is the design of model-specific validation experiments to provide observational data reflecting, to some extent, the structure of the target prediction. The applications of these frameworks in multiscale modeling of microscale materials, subject-specific treatments of brain tumors, and neural network surrogate models of mesoporous building insulation components will be discussed.

Bio: 

Danial Faghihi is an assistant professor in the Department of Mechanical Engineering at the University at Buffalo (UB) and holds an affiliated position in the Department of Civil, Structural and Environmental Engineering, as well as the Institute for Artificial Intelligence and Data Science. Prior to joining UB in 2019, he served as a research scientist at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. He earned his Ph.D. in structural engineering and mechanics from Louisiana State University. Dr. Faghihi's research centers on predictive computational modeling of complex materials and biological systems. He specializes in developing innovative, efficient, and scalable computational frameworks at the interface of physics-based modeling, scientific machine learning, and high-performance computing. Dr. Faghihi received the National Science Foundation CAREER Award in 2022 and has authored 35 journal articles in the field of computational mechanics.