Gianmarco Mengaldo, National University of Singapore
Wrik Mallik, University of Glasgow
Rajeev Jaiman, University of British Columbia, Vancouver
This mini-symposium focuses on the integration of physics and machine learning (ML) for computational mechanics and geophysical sciences. General-purpose black-box ML techniques are computationally efficient and scalable but may not perform well beyond the data they are being trained and they lack physical interpretability. To address these challenges, contributions and new achievements in algorithm design and software development for hybrid physics-based ML (PBML) techniques are solicited. Critical assessment and improving conventional ML techniques to develop accurate and robust surrogate models and the integration of model order reduction (MOR) with high-fidelity simulations and their application for real-world problems are appreciated. This mini-symposium aims to provide a platform for investigators to disseminate and discuss PBML and data-driven MOR techniques for prediction, analysis and design, especially in the context of geophysical science and various engineering applications.
