Deep Weak Approximation of SDEs: A Spatial Approximation Scheme for Solving Kolmogorov Equations
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Publication:6173002
DOI10.1142/s0219876221420147OpenAlexW4280594801MaRDI QIDQ6173002
Publication date: 21 July 2023
Published in: International Journal of Computational Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0219876221420147
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Cites Work
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- The Pricing of Options and Corporate Liabilities
- Numerical solution of singularly perturbed convection-diffusion-reaction problems with two small parameters
- A weak approximation with asymptotic expansion and multidimensional Malliavin weights
- Optimal error estimate using mesh equidistribution technique for singularly perturbed system of reaction-diffusion boundary-value problems
- Comparison of a priori and a posteriori meshes for singularly perturbed nonlinear parameterized problems
- Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
- Large deviations and asymptotic methods in finance
- Stochastic methods. A handbook for the natural and social sciences
- DGM: a deep learning algorithm for solving partial differential equations
- Higher order accurate approximations on equidistributed meshes for boundary layer originated mixed type reaction diffusion systems with multiple scale nature
- Solving the Kolmogorov PDE by means of deep learning
- On the approximate solutions of a class of fractional order nonlinear Volterra integro-differential initial value problems and boundary value problems of first kind and their convergence analysis
- A moving mesh refinement based optimal accurate uniformly convergent computational method for a parabolic system of boundary layer originated reaction-diffusion problems with arbitrary small diffusion terms
- DNN expression rate analysis of high-dimensional PDEs: application to option pricing
- Solving high-dimensional eigenvalue problems using deep neural networks: a diffusion Monte Carlo like approach
- A higher order weak approximation of McKean-Vlasov type SDEs
- A new efficient approximation scheme for solving high-dimensional semilinear PDEs: control variate method for deep BSDE solver
- Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms
- Solving many-electron Schrödinger equation using deep neural networks
- Convergence of the deep BSDE method for coupled FBSDEs
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A third-order weak approximation of multidimensional Itô stochastic differential equations
- Asymptotic expansion as prior knowledge in deep learning method for high dimensional BSDEs
- Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations
- Loss of regularity for Kolmogorov equations
- A numerical method for solving boundary and interior layers dominated parabolic problems with discontinuous convection coefficient and source terms
- An a posteriori based convergence analysis for a nonlinear singularly perturbed system of delay differential equations on an adaptive mesh
- Parameter uniform optimal order numerical approximation of a class of singularly perturbed system of reaction diffusion problems involving a small perturbation parameter
- Continuous Markov processes and stochastic equations
- Space-time error estimates for deep neural network approximations for differential equations
- An overview on deep learning-based approximation methods for partial differential equations
- A Theory of the Term Structure of Interest Rates
- The Malliavin Calculus and Related Topics
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
- Neural Networks and Deep Learning
- An Arbitrary High Order Weak Approximation of SDE and Malliavin Monte Carlo: Analysis of Probability Distribution Functions
- Numerical treatment of two‐parameter singularly perturbed parabolic convection diffusion problems with non‐smooth data
- Operator splitting around Euler–Maruyama scheme and high order discretization of heat kernels
- Deep Splitting Method for Parabolic PDEs
- Deep backward schemes for high-dimensional nonlinear PDEs
- Solving high-dimensional partial differential equations using deep learning
- Theoretical prospects of fractional order weakly singular Volterra Integro differential equations and their approximations with convergence analysis
- Solving high-dimensional optimal stopping problems using deep learning
- Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning
- A perturbation-based approach for solving fractional-order Volterra–Fredholm integro differential equations and its convergence analysis
- Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black--Scholes Partial Differential Equations
- A graded mesh refinement approach for boundary layer originated singularly perturbed time‐delayed parabolic convection diffusion problems
- Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations
- Stochastic Processes and Applications
- A generalized mean-reverting equation and applications
- Deep Learning Architectures
- A higher order difference method for singularly perturbed parabolic partial differential equations
- A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
- Deep optimal stopping
- Applied Stochastic Analysis
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