18 janvier 2019
in Publications, Rapports
This paper presents several numerical applications of deep learning-based algorithms that have been analyzed in . Numerical and comparative tests using TensorFlow illustrate the performance of our different algorithms, namely control learning by performance iteration (algorithms NNcontPI and ClassifPI), control learning by hybrid iteration (algorithms Hybrid-Now and Hybrid-LaterQ), on the 100-dimensional nonlinear PDEs examples from  and on quadratic Backward Stochastic Differential equations as in . We also provide numerical results for an option hedging problem in finance, and energy storage problems arising in the valuation of gas storage and in microgrid management.