A Deep Learning Scheme for Solving Fully Nonlinear PDEs with Maxim Bichuch @SIAMConnect
A Deep Learning Scheme for Solving Fully Nonlinear PDEs with Maxim Bichuch  @SIAMConnect
Uploaded October 2024 | Updated September 2026, 1 week ago
Watch the latest presentation of the SIAM Activity Group on FME Virtual Talk Series on a deep learning scheme for solving fully nonlinear partial differential equations with Maxim Bichuch of the State University of New York at Buffalo! Learn more about the SIAM Activity Group on Financial Mathematics and Engineering: siam.org/membership/activity-groups/detail/financial-mathematics-and-engineering.

Abstract: We study the convergence of a deep learning algorithm applied to a general class of fully nonlinear second order Partial Differential Equations. By using a suitable finite difference approximation to the loss function of the deep learning scheme we show the convergence of the numerical solution to the unique viscosity solution. We apply our results and illustrate this convergence to the finite horizon optimal investment problem with proportional transaction costs in single and multi-asset settings.

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A Deep Learning Scheme for Solving Fully Nonlinear PDEs with Maxim Bichuch

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