Advancing Machine Learning for Floating Offshore Wind: Challenges and Opportunities in Wave-Structure Interaction

Scientific Machine Learning for Wave-Structure Interaction Modelling: Uncertainty Quantification, Predictable Zone, and Structural Responses – Dr. Jincheng Zhang is an Assistant Professor at the School of Engineering, University of Warwick. He obtained his B.S. and M.S. degrees in Mechanical Engineering from Tsinghua University in 2015 and 2018, respectively, and his PhD from the University of Warwick in 2021. His research focuses on data-driven and physics-informed deep learning, wave and wind energy, CFD simulations, and uncertainty quantification.

Foundational AI Directions for CFD and Multi-Physics – Prof Christopher Pain (Earth Science and Engineering Department, Imperial College London) leads the Applied Modelling and Computation Group (AMCG) at ICL. He is the director of the data assimilation lab in the Data Science Institute (DSI) at ICL and is co-director of the Centre for AI-Physics Modelling at Imperial-X. His interests are in foundational AI modelling for the environment, health and wellbeing, energy and industry.

Abstract: Recent developments in AI are transforming a large number of fields and are now starting to make a major impact in computational physics. Here we describe some of these innovative AI techniques that have been recently developed and how they can work together. We will indicate how AI may be deployed for modelling of environmental flows. Recent advances have enabled AI software to solve, to within numerical tolerances, the discrete differential equations that govern the physics of fluids (AI4PDEs). Important also is the use of AI to solve particle systems (e.g. AI for Discrete Element Modelling (AI4DEM)) which are expressed by interparticle forces and Lagrangian particle motion. The presentation will provide a summary of these forward models and a view on how these new approaches can be used with trained foundational AI models to form even more powerful methods.

Bayesian and Surrogate-Assisted Optimisation for CFD – Professor Gavin Tabor is a member of the Centre for Water Systems and the Computational Engineering group. He graduated from Christs College Cambridge in 1990 with a 1st in Theoretical Physics, then did a Ph.D. in Theoretical Astrophysics at the Department of Physics at Oxford. Changing both location and research area, he then worked as a RA in Prof. David Gosman’s research group at Imperial College, London, for 5 years. During this time he worked on Computational Fluid Dynamics (CFD) of multiphase flows and the modelling of premixed turbulent combustion, and contributed towards the CFD code now known as OpenFOAM. He is  a Fellow of the Institute of Physics and member of the Computational Physics Group committee of the IOP, Chair of the Joint Technical Committees for the OpenFOAM Governance effort, and a member of the international OpenFOAM Workshop Committee.