DeepBSDEAnsätzen
DeepBSDEAnsätzen refers to a class of numerical methods for solving high-dimensional Partial Differential Equations (PDEs) and Backward Stochastic Differential Equations (BSDEs). These methods leverage deep neural networks to approximate the solution of these equations. The core idea is to represent the unknown solution function, which might be a complex, high-dimensional object, as a neural network. The parameters of this neural network are then trained by minimizing a loss function that enforces the PDE or BSDE conditions.
The training process typically involves sampling points in the domain of the equation and using the neural
DeepBSDEAnsätzen are particularly attractive for problems where traditional numerical methods, such as finite differences or finite