Iterative surrogate model optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks
Kjetil Olsen Lye, Siddhartha Mishra, Deep Ray, Praveen Chandrashekar
Abstract
We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems. This algorithm is based on deep neural networks and its key feature is the iterative selection of training data through a feedback loop between deep neural networks and any underlying standard optimization algorithm. Numerical examples for optimal control, parameter identification and shape optimization problems for PDEs are provided to demonstrate that ISMO significantly outperforms a standard deep neural network based surrogate optimization algorithm as well as standard optimization algorithms.