Abstract

Machine learning methods for learning physical models, in particular (partial) differential equations, has been a popular topic nowadays. In this talk, we consider optimal control of such learning-informed models with neural networks as some nonlinear components. Particularly, we present their numerical algorithms and approximation properties. We will provide both analytical and numerical aspects of such models, and show their applications in optimal control of partial differential equations and quantitative magnetic resonance imaging.