ERC Consolidator Grant · 2021
Overcoming the curse of dimensionality through nonlinear stochastic algorithms
In a series of relevant real world problems it is of fundamental importance to approximatively compute evaluations of high-dimensional functions. Such high-dimensional approximation problems appear, e.g., in stochastic optimal control problems in operations research, e.g., in supervised learning problems, e.g., in financial engineering where partial differential equations (PDEs) and forward backward stochastic differential equations (FBSDEs) are used to approximatively price financial products, and, e.g., in nonlinear filtering problems where stochastic PDEs are used to approximatively describe the state of a given physical system with only partial information available. Standard…
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