Publication:5043153
From MaRDI portal
zbMath1498.65021MaRDI QIDQ5043153
Publication date: 21 October 2022
Full work available at URL: https://gradmath.org/2021/05/24/machine-learning-approximations-for-some-parabolic-partial-differential-equations/
Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Learning and adaptive systems in artificial intelligence (68T05) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30)
Cites Work
- Unnamed Item
- Optimal stochastic control, stochastic target problems, and backward SDE.
- Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
- Backward SDEs with constrained jumps and quasi-variational inequalities
- Continuous-time stochastic control and optimization with financial applications
- Monotonic limit theorem of BSDE and nonlinear decomposition theorem of Doob-Meyer's type
- On best approximation by ridge functions
- Lower bounds for approximation by MLP neural networks
- Approximation and estimation bounds for artificial neural networks
- Reflected solutions of backward SDE's, and related obstacle problems for PDE's
- Probabilistic interpretation of a system of semilinear parabolic partial differential equations
- Multilayer feedforward networks are universal approximators
- Numerical probability. An introduction with applications to finance
- Constrained BSDEs, viscosity solutions of variational inequalities and their applications
- Discretization and machine learning approximation of BSDEs with a constraint on the gains-process
- Universal approximation bounds for superpositions of a sigmoidal function
- Backward Stochastic Differential Equations in Finance
- Deep backward schemes for high-dimensional nonlinear PDEs
- Solving high-dimensional partial differential equations using deep learning
- Deep optimal stopping
- Convex Analysis
- Approximation by superpositions of a sigmoidal function
