Deep neural networks based temporal-difference methods for high-dimensional parabolic partial differential equations
DOI10.1016/j.jcp.2022.111503OpenAlexW4288458715WikidataQ114163217 ScholiaQ114163217MaRDI QIDQ2168314
Yihua Cai, Shaojie Zeng, Qingsong Zou
Publication date: 31 August 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2022.111503
neural networkforward-backward stochastic differential equationstemporal-difference learninghigh-dimensional PDEs
Stochastic analysis (60Hxx) Artificial intelligence (68Txx) Partial differential equations of mathematical physics and other areas of application (35Qxx)
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Cites Work
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- DiffSharp
- Numerical simulation of quadratic BSDEs
- 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
- Multilayer feedforward networks are universal approximators
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- Functional multi-layer perceptron: A nonlinear tool for functional data analysis
- DGM: a deep learning algorithm for solving partial differential equations
- nPINNs: nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications
- B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
- PFNN: a penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries
- SPINN: sparse, physics-based, and partially interpretable neural networks for PDEs
- A new efficient approximation scheme for solving high-dimensional semilinear PDEs: control variate method for deep BSDE solver
- Adversarial uncertainty quantification in physics-informed neural networks
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Machine learning for semi linear PDEs
- Linear regression MDP scheme for discrete backward stochastic differential equations under general conditions
- Nonlinear Partial Differential Equations for Scientists and Engineers
- Second-order backward stochastic differential equations and fully nonlinear parabolic PDEs
- Deep Splitting Method for Parabolic PDEs
- Deep backward schemes for high-dimensional nonlinear PDEs
- Solving high-dimensional partial differential equations using deep learning
- Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions
- Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
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