Pages that link to "Item:Q4967451"
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The following pages link to Solving high-dimensional partial differential equations using deep learning (Q4967451):
Displaying 50 items.
- Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations (Q5161194) (← links)
- Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units (Q5162006) (← links)
- Machine Learning and Computational Mathematics (Q5162355) (← links)
- Deep Network Approximation Characterized by Number of Neurons (Q5162359) (← links)
- Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains (Q5162368) (← links)
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations (Q5162369) (← links)
- On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs (Q5162370) (← links)
- Numerical Simulations for Full History Recursive Multilevel Picard Approximations for Systems of High-Dimensional Partial Differential Equations (Q5162373) (← links)
- Multi-Scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains (Q5162374) (← links)
- Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning (Q5162375) (← links)
- An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems (Q5162376) (← links)
- Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection (Q5162626) (← links)
- Short Communication: A Quantum Algorithm for Linear PDEs Arising in Finance (Q5162857) (← links)
- Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks (Q5163210) (← links)
- Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions (Q5163229) (← links)
- Solving Fokker-Planck equation using deep learning (Q5218164) (← links)
- A Multiscale Neural Network Based on Hierarchical Matrices (Q5222107) (← links)
- SwitchNet: A Neural Network Model for Forward and Inverse Scattering Problems (Q5240806) (← links)
- Using machine learning to predict extreme events in complex systems (Q5854795) (← links)
- Strong Solutions for PDE-Based Tomography by Unsupervised Learning (Q5860278) (← links)
- A Local Deep Learning Method for Solving High Order Partial Differential Equations (Q5864768) (← links)
- Approximative Policy Iteration for Exit Time Feedback Control Problems Driven by Stochastic Differential Equations using Tensor Train Format (Q5865245) (← links)
- Deep neural networks can stably solve high-dimensional, noisy, non-linear inverse problems (Q5873926) (← links)
- Numerical approximations of coupled forward–backward SPDEs (Q5880399) (← links)
- Sparse Deep Neural Network for Nonlinear Partial Differential Equations (Q5885722) (← links)
- Stationary Density Estimation of Itô Diffusions Using Deep Learning (Q5886225) (← links)
- Convergence of a Robust Deep FBSDE Method for Stochastic Control (Q5886857) (← links)
- A Regularity Theory for Static Schrödinger Equations on \(\boldsymbol{\mathbb{R}}\)<sup><i>d</i></sup> in Spectral Barron Spaces (Q5887733) (← links)
- A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations (Q5889064) (← links)
- A deep learning method for solving high-order nonlinear soliton equations (Q6039957) (← links)
- Value-Gradient Based Formulation of Optimal Control Problem and Machine Learning Algorithm (Q6040292) (← links)
- Deep learning neural networks for the third-order nonlinear Schrödinger equation: bright solitons, breathers, and rogue waves (Q6046358) (← links)
- Data-driven hedging of stock index options via deep learning (Q6047693) (← links)
- Solving second-order nonlinear evolution partial differential equations using deep learning (Q6048188) (← links)
- Learning-based local weighted least squares for algebraic multigrid method (Q6048416) (← links)
- Numerical Solution of the Incompressible Navier-Stokes Equation by a Deep Branching Algorithm (Q6049610) (← links)
- Simultaneous neural network approximation for smooth functions (Q6052416) (← links)
- Data-Driven Tensor Train Gradient Cross Approximation for Hamilton–Jacobi–Bellman Equations (Q6054276) (← links)
- Deep Neural Networks for Solving Large Linear Systems Arising from High-Dimensional Problems (Q6054285) (← links)
- Control variate method for deep BSDE solver using weak approximation (Q6054320) (← links)
- Asset pricing with general transaction costs: Theory and numerics (Q6054360) (← links)
- Deep empirical risk minimization in finance: Looking into the future (Q6054448) (← links)
- On the approximation of functions by tanh neural networks (Q6055124) (← links)
- Numerical solution of nonlinear stochastic differential equations with fractional Brownian motion using fractional-order Genocchi deep neural networks (Q6058729) (← links)
- Randomized neural network with Petrov-Galerkin methods for solving linear and nonlinear partial differential equations (Q6058946) (← links)
- Probabilistic partition of unity networks for high‐dimensional regression problems (Q6062830) (← links)
- An introduction to the mathematics of deep learning (Q6064555) (← links)
- DEEP EQUILIBRIUM NETS (Q6067145) (← links)
- Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
- Physics-Informed Neural Networks for Solving Dynamic Two-Phase Interface Problems (Q6068803) (← links)
