Leveraging Physics-Induced Bias in Scientific Machine Learning for Computational Mechanics

Explore a comprehensive lecture on leveraging physics-induced bias in scientific machine learning for computational mechanics, focusing on physics-informed, structure-preserved learning for problems with irregular geometries. Delve into advanced concepts presented by Jianxun Wang at the Alan Turing Institute, offering valuable insights for researchers and practitioners in the field of scientific computing and machine learning. Gain a deeper understanding of how to incorporate physical principles into machine learning models to enhance their performance and accuracy when dealing with complex geometrical structures in computational mechanics problems.