Pages that link to "Item:Q2123831"
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The following pages link to Solving high-dimensional eigenvalue problems using deep neural networks: a diffusion Monte Carlo like approach (Q2123831):
Displaying 27 items.
- Recurrent neural networks for stochastic control problems with delay (Q2061009) (← links)
- A new efficient approximation scheme for solving high-dimensional semilinear PDEs: control variate method for deep BSDE solver (Q2133701) (← links)
- A semigroup method for high dimensional elliptic PDEs and eigenvalue problems based on neural networks (Q2135256) (← links)
- Solving eigenvalue PDEs of metastable diffusion processes using artificial neural networks (Q2157080) (← links)
- Solving elliptic equations with Brownian motion: bias reduction and temporal difference learning (Q2157396) (← links)
- Convergence of deep fictitious play for stochastic differential games (Q2170300) (← links)
- An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
- Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
- Actor-Critic Method for High Dimensional Static Hamilton--Jacobi--Bellman Partial Differential Equations based on Neural Networks (Q5021407) (← links)
- A Deep Learning Method for Elliptic Hemivariational Inequalities (Q5074898) (← links)
- Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning (Q6047503) (← links)
- Neural networks based on power method and inverse power method for solving linear eigenvalue problems (Q6052345) (← links)
- Control variate method for deep BSDE solver using weak approximation (Q6054320) (← links)
- A priori generalization error analysis of two-layer neural networks for solving high dimensional Schrödinger eigenvalue problems (Q6076649) (← links)
- Predicting rare events using neural networks and short-trajectory data (Q6104999) (← links)
- A deep learning method for computing eigenvalues of the fractional Schrödinger operator (Q6130977) (← links)
- A dive into spectral inference networks: improved algorithms for self-supervised learning of continuous spectral representations (Q6132297) (← links)
- Asymptotic-preserving neural networks for multiscale time-dependent linear transport equations (Q6158979) (← links)
- Deep Weak Approximation of SDEs: A Spatial Approximation Scheme for Solving Kolmogorov Equations (Q6173002) (← links)
- Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing (Q6178392) (← links)
- Deep Ritz method for elliptical multiple eigenvalue problems (Q6182319) (← links)
- Commutation of geometry-grids and fast discrete PDE eigen-solver GPA (Q6183897) (← links)
- A deep-genetic algorithm (deep-GA) approach for high-dimensional nonlinear parabolic partial differential equations (Q6184720) (← links)
- Neural Control of Parametric Solutions for High-Dimensional Evolution PDEs (Q6194975) (← links)
- Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach (Q6334288) (← links)
- Approximating the stationary Bellman equation by hierarchical tensor products (Q6616995) (← links)
- Analysis of deep Ritz methods for semilinear elliptic equations (Q6662390) (← links)
