375 citations · 608 across the 47 of their papers we have counts for
10 papers · 1 filter
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…
Rule-based Strategies for Dynamic Life Cycle Investment
T. R. B. den Haan, K. W. Chau, M. van der Schans +1
In this work, we consider rule-based investment strategies for managing a defined contribution saving scheme under the Dutch pension fund testing model. We found that dynamic rule-…
Deep learning for CVA computations of large portfolios of financial derivatives
Kristoffer Andersson, Cornelis W. Oosterlee
In this paper, we propose a neural network-based method for CVA computations of a portfolio of derivatives. In particular, we focus on portfolios consisting of a combination of der…
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…
The Seven-League Scheme: Deep learning for large time step Monte Carlo simulations of stochastic differential equations
Shuaiqiang Liu, Lech A. Grzelak, Cornelis W. Oosterlee
We propose an accurate data-driven numerical scheme to solve Stochastic Differential Equations (SDEs), by taking large time steps. The SDE discretization is built up by means of a…
Financial option valuation by unsupervised learning with artificial neural networks
Beatriz Salvador, Cornelis W. Oosterlee, Remco van der Meer
Artificial neural networks (ANNs) have recently also been applied to solve partial differential equations (PDEs). In this work, the classical problem of pricing European and Americ…