A Physics-guided Machine Learning Model Based on Peridynamics

Speaker: Erkan Oterkus (Department of Naval Architecture, Ocean, and Marine Engineering; University of Strathclyde, UK)
Title: A Physics-guided Machine Learning Model Based on Peridynamics
Time: Wednesday, 2020.12.16, 10:00 a.m. (CET)
Place: fully virtual (contact Dr. Jakub Lengiewicz to register)
Format: 30 min. presentation + 30 min. discussion

Abstract: 

With the rapid growth of available data and computing resources, using data-driven models is a potential approach in many scientific disciplines and engineering. However, for complex physical phenomena that have limited data, the data-driven models are lacking robustness and fail to provide good predictions. Theory-guided data science is the recent technology that can take advantage of both physics-driven and data-driven models. In this webinar, a new physics-guided machine learning model based on peridynamics will be presented. Peridynamics is a suitable approach for predicting progressive damages because the theory uses integro-differential equations instead of partial differential equations. Several numerical examples will be shown to demonstrate the capability of the methodology.