Temporal difference learning for high-dimensional PIDEs with jumps
DOI10.1137/23M1584538zbMATH Open1545.65044MaRDI QIDQ6575343
Hailong Guo, Liwei Lu, Xu Yang, Yi Zhu
Publication date: 19 July 2024
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Artificial neural networks and deep learning (68T07) Numerical methods for integral equations (65R20) Integro-partial differential equations (45K05) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30) Stochastic integral equations (60H20) Jump processes on general state spaces (60J76)
Cites Work
- Title not available (Why is that?)
- Backward stochastic differential equations with jumps and their actuarial and financial applications. BSDEs with jumps
- Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
- Weak adversarial networks for high-dimensional partial differential equations
- A nonlinear partial integro-differential equation from mathematical finance
- Non-local partial differential equations for engineering and biology. Mathematical modeling and analysis
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- Optimal error estimates of two mixed finite element methods for parabolic integro-differential equations with nonsmooth initial data
- DGM: a deep learning algorithm for solving partial differential equations
- \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition
- Deep learning schemes for parabolic nonlocal integro-differential equations
- Self-adaptive physics-informed neural networks
- MIM: a deep mixed residual method for solving high-order partial differential equations
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
- CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method
- Scientific machine learning through physics-informed neural networks: where we are and what's next
- Deep neural networks based temporal-difference methods for high-dimensional parabolic partial differential equations
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A finite elements approach for spread contract valuation via associated two-dimensional PIDE
- A system of non-local parabolic PDE and application to option pricing
- A Second-order Finite Difference Method for Option Pricing Under Jump-diffusion Models
- Lévy Processes and Stochastic Calculus
- Solving high-dimensional partial differential equations using deep learning
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks
- Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions
- A Finite Difference Scheme for Option Pricing in Jump Diffusion and Exponential Lévy Models
- Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
This page was built for publication: Temporal difference learning for high-dimensional PIDEs with jumps
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6575343)