Stochastic reduced-order modeling with applications

Jared Callaham
University of Washington
Mechanical Engineering

Data-driven methods for approximating complex systems with stochastic models. Techniques are fairly general, but motivated by fluid mechanics.


Back to Machine Learning for Physics and the Physics of Learning