Date and Time: Friday, November 19, 11:00-11:59 am

Speaker: Maziar Raissi

Place: zoom

Zoom link:

https://gwu-edu.zoom.us/j/92385444375?pwd=SktyVElKc2VPOUc1RzN1bVM2WnlOZz09

Title: Data-Efficient Deep Learning using Physics-Informed Neural Networks

Abstract: A grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws, physical

principles, and/or phenomenological behaviors expressed by differential equations with the vast data sets available in many fields of engineering, science, and technology. At the intersection of probabilistic machine learning, deep learning, and scientific computations, this work is pursuing the overall vision to establish

promising new directions for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics

to design learning machines with the ability to operate in complex domains without requiring large quantities of data. To materialize this vision, this work is exploring two complementary directions: (1) designing data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and non-linear differential equations, to extract patterns from high-dimensional data generated from experiments, and (2) designing novel numerical

algorithms that can seamlessly blend equations and noisy multi-fidelity data, infer latent quantities of interest (e.g., the solution to a differential equation), and naturally quantify uncertainty in computations.