2 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
The One Step Malliavin scheme: new discretization of BSDEs implemented with deep learning regressions
Balint Negyesi, Kristoffer Andersson, Cornelis W. Oosterlee
A novel discretization is presented for forward-backward stochastic differential equations (FBSDE) with differentiable coefficients, simultaneously solving the BSDE and its Malliav…
Reduced Order Modeling for Parameterized Time-Dependent PDEs using Spatially and Memory Aware Deep Learning
Nikolaj T. Mücke, Sander M. Bohté, Cornelis W. Oosterlee
We present a novel reduced order model (ROM) approach for parameterized time-dependent PDEs based on modern learning. The ROM is suitable for multi-query problems and is nonintrusi…
On high-order schemes for tempered fractional partial differential equations
Linlin Bu, Cornelis W. Oosterlee
In this paper, we propose third-order semi-discretized schemes in space based on the tempered weighted and shifted Grünwald difference (tempered-WSGD) operators for the tempered fr…
Optimally weighted loss functions for solving PDEs with Neural Networks
Remco van der Meer, Cornelis Oosterlee, Anastasia Borovykh
Recent works have shown that deep neural networks can be employed to solve partial differential equations, giving rise to the framework of physics informed neural networks. We intr…
Exploration of a Cosine Expansion Lattice Scheme
Ki Wai Chau, Cornelis W. Oosterlee
In this article, we combine a lattice sequence from Quasi-Monte Carlo rules with the philosophy of the Fourier-cosine method to design an approximation scheme for expectation compu…