Dr. Danial Faghihi

Recent advances in computational science have enabled harnessing large-scale data from measurements, images, or high-fidelity simulations to advance the computational prediction of complex physical systems for making high-consequence decisions. Overriding importance in scientific prediction is validating mechanistic models in the presence of uncertainties. In addition to data uncertainty and modeling error, uncertainties in selecting the model formulation pose a significant challenge to predictive computational modeling. This talk discusses Occam-Plausibility ALgorithm (OPAL), a systematic strategy for selecting an "optimal" predictive model among the numerous possible models with different fidelities and complexities that delivers sufficiently accurate computational prediction. OPAL leverages Bayesian inference and the notion of model plausibility along with the design of model-specific validation experiments to provide observational data reflecting, in some sense, the structure of the target prediction. An application of this framework in selecting an optimal discrete-to-continuum multiscale model for predicting the performance of microscale materials systems will be presented. Moreover, leveraging the validated models for predicting heterogeneous tumor morphology in specific subjects via a scalable high-dimensional Bayesian inference will also be presented. Finally, a method and efficient algorithm for mitigating the uncertainty during design decision-making will be discussed in the context of multiphase models of mesoporous materials for buildings' superinsulation components.

Danial Faghihi is currently an assistant professor in the Department of Mechanical Engineering and holds an affiliated position at the Department of Civil, Structural and Environmental Engineering at the University at Buffalo (UB). Before joining UB in 2019, he was a research scientist at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. He obtained his Ph.D. in structural engineering and mechanics from Louisiana State University. Dr. Faghihi’s research interests focus on the predictive multiscale modeling of complex materials and biological systems. In particular, he is interested in developing novel computational frameworks at the interface of physics-based models, scientific machine learning methods, and high-performance computing. Dr. Faghihi is the recipient of the National Science Foundation CAREER Award in 2022 and has published 32 journal articles in the field of computational mechanics.