The School of Mechanical and Materials Engineering Seminar Series, “Generative Learning and Uncertainty Quantification on Manifolds for Scientific Discovery” Presented by Dr. Dimitris Giovanis

About the event

Generative Learning and Uncertainty Quantification on Manifolds for Scientific Discovery

Presented by

Dr. Dimitris Giovanis, Assistant Research Professor, Department of Civil & Systems Engineering, John Hopkins University

Abstract

We are integrating modern tools from data science and scientific machine learning with uncertainty quantification to accelerate and optimize simulation and analysis for scientific discovery and data-informed design, optimization, and decision making in science and engineering. In mathematical modeling of complex systems, one typically progresses from observations of the world first to equations for a model, and then to the analysis of the model to make predictions. However, several major challenges arise in this process since the complex high-dimensional physical systems of interest often involve multiple physics, multiple length- and time-scales, as well as nonlinear and history dependent behaviors. To further complicate matters, physics-based models must contend with a myriad of uncertainties such as inherent stochasticity in the physics (aleatory uncertainty) and uncertainties in model-form (epistemic uncertainties). In this talk we discuss how modern machine learning approaches like Deep Learning, Generative/Diffusion Models combined with Manifold Learning can be leveraged to develop reduced-order/surrogate models and sampling strategies that expedite simulation and uncertainty quantification in computational science and engineering.

Biography

Dimitris Giovanis is an Assistant Research Professor in the Dept. of Civil & Systems Engineering at Johns Hopkins University. Dr. Giovanis develops computational tools and algorithms that combine scientific machine learning with modeling on low-dimensional manifolds and uncertainty quantification, to push the envelope of both traditional modeling and physics-informed scientific machine learning to very high-dimensional and complex physical systems in which computational efficiency is critical, and their behavior is highly unpredictable. His work spans applications in materials in extreme environments, natural hazards, biomechanics/bioengineering, aerospace, and epidemics.  His Ph.D. was on stochastic finite element methods, he has a master’s degree in computational mechanics and holds a five-year diploma in Civil (Structural) Engineering, all from the National Technical University of Athens. He is affiliated with the Center on Artificial Intelligence for Materials in Extreme Environments (CAIMEE), the Hopkins Extreme Materials Institute (HEMI), the Mathematical Institute for Data Science (MINDS), the Institute for Data Intensive Engineering and Science (IDIES), and the NHERI’s SimCenter. He is a member of the ASCE/EMI Probabilistic Methods Committee, the ASCE/EMI Machine Learning Group, and member of the SIAM/UQ group. He is also an Assistant Coach for the JHU men’s Water Polo team.