A deep neural network algorithm for semilinear elliptic PDEs with applications in insurance mathematics

S Kremsner, A Steinicke, M Szölgyenyi - Risks, 2020 - mdpi.com
In insurance mathematics, optimal control problems over an infinite time horizon arise when
computing risk measures. An example of such a risk measure is the expected discounted …

Barrier options and Greeks: Modeling with neural networks

N Umeorah, P Mashele, O Agbaeze, JC Mba - Axioms, 2023 - mdpi.com
This paper proposes a non-parametric technique of option valuation and hedging. Here, we
replicate the extended Black–Scholes pricing model for the exotic barrier options and their …

Comparison of statistical approaches for reconstructing random coefficients in the problem of stochastic modeling of air–sea heat flux increments

KP Belyaev, AK Gorshenin, VY Korolev, AA Osipova - Mathematics, 2024 - mdpi.com
This paper compares two statistical methods for parameter reconstruction (random drift and
diffusion coefficients of the Itô stochastic differential equation, SDE) in the problem of …

The Seven-League Scheme: Deep learning for large time step Monte Carlo simulations of stochastic differential equations

S Liu, LA Grzelak, CW Oosterlee - Risks, 2022 - mdpi.com
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 Deep neural network approach to solving for seal's type partial integro-differential equation

B Su, C Xu, J Li - Mathematics, 2022 - mdpi.com
In this paper, we study the problem of solving Seal's type partial integro-differential
equations (PIDEs) for the classical compound Poisson risk model. A data-driven deep …

Deep FBSDE Neural Networks for Solving Incompressible Navier-Stokes Equation and Cahn-Hilliard Equation

Y Deng, Q He - arXiv preprint arXiv:2401.03427, 2024 - arxiv.org
Efficient algorithms for solving high-dimensional partial differential equations (PDEs) has
been an exceedingly difficult task for a long time, due to the curse of dimensionality. We …

Solve High-Dimensional Reflected Partial Differential Equations by Neural Network Method

X Shi, X Zhang, R Tang, J Yang - Mathematical and Computational …, 2023 - mdpi.com
Reflected partial differential equations (PDEs) have important applications in financial
mathematics, stochastic control, physics, and engineering. This paper aims to present a …