CCB Special Lecture Series with Michael Chertkov
Machine learning and artificial intelligence play an increasingly critical role in modern science. This lecture series will provide an introductory tutorial on these methods for researchers interested in a range of physical problems, and will give participants a basic understanding of the principles of current physics-informed machine learning approaches and introduce several widely-used techniques. Topics will include model reduction, deep learning approaches to differential equations, graphical model tools, and optimization methods.
|December 14, 2020||Application Agnostic Learning from Data: Questions, Formulations, Approaches|
|December 15, 2020||Setting Up Physics (Science) Informed (Explainable) Learning: Questions, Formulations|
|December 16, 2020||Physics Informed Model Reduction (e.g. testing, resolving or excluding models/theories)|
|January 5, 2021||Graphical Model Tools (i.e. formulations, methods, algorithms) for Inference, Learning, and Optimization (will allow to account most naturally physics constraints, relations, dependencies, etc)|
|January 6, 2021||Attempt of Synthesis|