Deep Learning algorithm for solving high-dimensional nonlinear PDEs in finance

We present a (random) neural networks based algorithm which can solve nonlinear PDEs to price high-dimensional financial derivatives under default risk.

Ariel Neufeld is a Tenured Associate Professor in mathematics at the Nanyang Technological University in Singapore. He received his PhD in mathematics in May 2015 at ETH Zurich, where he spent half of his PhD at Columbia University in the City of New York. Prior to joining NTU he was a postdoctoral researcher at ETH Zurich. His research focuses on machine learning algorithms and their applications in finance and insurance, model uncertainty in financial markets and distributionally robust optimization, as well as stochastic analysis and stochastic optimal control. He was awarded in 2021 with the SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize and recently with the Bruti-Liberati Visiting Fellowship Award.