Pages that link to "Item:Q2152480"
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The following pages link to Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms (Q2152480):
Displaying 25 items.
- DGM: a deep learning algorithm for solving partial differential equations (Q2002333) (← links)
- Efficient approximation of solutions of parametric linear transport equations by ReLU DNNs (Q2026114) (← links)
- Spectral methods for nonlinear functionals and functional differential equations (Q2028689) (← links)
- Solving the Kolmogorov PDE by means of deep learning (Q2051092) (← links)
- Proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients (Q2057087) (← links)
- DNN expression rate analysis of high-dimensional PDEs: application to option pricing (Q2117328) (← links)
- Self-adaptive deep neural network: numerical approximation to functions and PDEs (Q2133768) (← links)
- Overcoming the curse of dimensionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities (Q2162115) (← links)
- Solving for high-dimensional committor functions using artificial neural networks (Q2319851) (← links)
- Approximation properties of residual neural networks for Kolmogorov PDEs (Q2697245) (← links)
- An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
- A QMC-Deep Learning Method for Diffusivity Estimation in Random Domains (Q4996838) (← links)
- Hybrid method based on neural networks and Monte Carlo simulation in view of a tradeoff between accuracy and computational time (Q5079831) (← links)
- Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions (Q5093100) (← links)
- Unbiased Deep Solvers for Linear Parametric PDEs (Q5093244) (← links)
- Deep learning-based approximation of Goldbach partition function (Q5101877) (← links)
- A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations (Q5889064) (← links)
- Deep ReLU neural network approximation in Bochner spaces and applications to parametric PDEs (Q6062166) (← links)
- Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
- Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation (Q6107984) (← links)
- Lower bounds for artificial neural network approximations: a proof that shallow neural networks fail to overcome the curse of dimensionality (Q6155895) (← links)
- Deep Weak Approximation of SDEs: A Spatial Approximation Scheme for Solving Kolmogorov Equations (Q6173002) (← links)
- Mobility Estimation for Langevin Dynamics Using Control Variates (Q6178098) (← links)
- Strong overall error analysis for the training of artificial neural networks via random initializations (Q6617376) (← links)
- Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations (Q6645961) (← links)
