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Found 4 papers, 2 papers with code
Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs with infinite activity
In this paper, we present a randomized extension of the deep splitting algorithm introduced in [Beck, Becker, Cheridito, Jentzen, and Neufeld (2021)] using random neural networks suitable to approximately solve both high-dimensional nonlinear parabolic PDEs and PIDEs with jumps having (possibly) infinite activity.
Universal Approximation Property of Random Neural Networks
In this paper, we study random neural networks which are single-hidden-layer feedforward neural networks whose weights and biases are randomly initialized.
Global universal approximation of functional input maps on weighted spaces
This then applies in particular to approximation of (non-anticipative) path space functionals via functional input neural networks.
Chaotic Hedging with Iterated Integrals and Neural Networks
In this paper, we extend the Wiener-Ito chaos decomposition to the class of diffusion processes, whose drift and diffusion coefficient are of linear growth.
