Erkan Oterkus: A Physics-guided Machine Learning Model Based on Peridynamics

Machine Learning Seminar presentation

Topic: A Physics-guided Machine Learning Model Based on Peridynamics

Speaker: Pofessor Erkan Oterkus, PeriDynamics Research Center, University of Strathclyde, Glasgow, UK

Time: Wednesday 2020.12.16, 10:00 CET

How to join: Please contact Jakub Lengiewicz


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.

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