Data-driven model discovery: Targeted use of deep neural networks for physics and engineering
website: faculty.washington.edu/kutz
This video highlights physics-informed machine learning architectures that allow for the simultaneous discovery of physics models and their associated coordinate systems from data alone. The targeted use of neural networks and enforcement of parsimonious models allows for a robust architecture that can be broadly applied in the sciences.