Upscaling & Automation: Pushing Boundaries of Multiscale Modeling thru Symbolic-Numeric Computing

"Upscaling and Automation: Pushing the Boundaries of Multiscale Modeling through Symbolic-Numeric Computing" with Ilenia Battiato Energy Science and Engineering, Stanford University ABSTRACT Geologic porous media have played and continue to play a critical role in the energy transition. They are the physical domains from which resources can be harvested, where CO2 can be sequestered, and where H2 can be stored. Modeling and prediction of fluid flow and reacting species in the Earth's subsurface continues to be a major open challenge in computational physics because geologic porous media are inherently multiscale, and the relevant scales of interest can easily span 10 orders of magnitude. Despite advances in the development of multiscale models of flow and reactive transport in geologic porous media, significant challenges remain in the rigorous derivation of continuum models themselves and their numerical implementation and verification, particularly in presence of systems of realistic complexity (e.g., tens of reacting species/minerals). The derivation and numerical implementation of macroscopic models, from their fine-scale counterpart through formal upscaling techniques, can take many years to develop, even with concerted efforts of applied mathematicians, modelers, and computational physicists. These efforts can become daunting for systems of realistic complexity, such as reactive single- and multi-phase transport in geologic media. In contrast to data-driven methods, recent works have shown that recursive symbolic algorithms can be used to augment human deductive capabilities through the automation of rigorous (mathematical) upscaling theories, and can lead to scientific discoveries. Enlisting the machine to perform the time-consuming and error-prone procedures allows modelers to speed the time to derive upscaled equations by 5 orders of magnitude. The generated macroscopic systems of equations are accurate within estimated upscaling errors; however, the massive and complex differential equations are still difficult to handle for the purpose of numerical implementation and verification. To tackle this problem, Battiato and her team propose the first symbolic-numeric framework fully integrating automated symbolic deduction capabilities for multiscale model development and automated numerical code generation, which can be run with minimal human interaction. SPEAKER Dr. Ilenia Battiato is Associate Professor of Energy Science and Engineering at Stanford University and leads the Multiscale Physics in Energy Systems Laboratory. Battiato’s research focuses on understanding, modeling, and predicting complex multiscale multiphysics systems with cross-cutting applications in the energy landscape ranging from electrochemical storage to CO2 sequestration and H2 storage in the subsurface. She uses a combination of rigorous mathematical theories, numerical computing, and symbolic deduction to develop advanced multiscale multiphysics models. Category: Structural; Mathematical modeling