Physics-informed Machine Learning (Φ-ML) Meets Engineering Webinar
Title: Physics-enhancing machine learning strategies in applied solid mechanics: recent advances
Abstract:
Machine Learning based techniques are widely used in applied solid mechanics for reducing computational costs, improving modelling and forecasting, and enabling efficient and accurate information extraction. However, we are often confronted with the challenges of (i) having access to a small volume of informative data, (ii) the need of embedding physics-based knowledge to enable generalization in the small/medium data cases, and (iii) to enforce physics-constraints with large data to ensure physically consistent predictions. Physics-informed Machine Learning (aka Scientific Machine Learning) poses an integration challenge that goes beyond data and physics-based models, and includes the quantification of uncertainty, interpretability and explainability of the results, and dealing with small heterogeneous, gappy and noisy data. This seminar will give an overview of the open challenges and of recent research work carried out within the Data, Vibration and Uncertainty Group for developing enhanced strategies in applied solid mechanics, with particular focus on structural health monitoring and friction force evaluation.
Speaker: Dr. Alice Cicirello
ZOOM link: https://turing-uk.zoom.us/j/99614752040?pwd=cVRaQW1Ndzd5MGpWQWdKLzA0QkUzUT09
ID: 996 1475 2040 / passcode: 435421
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