Pages that link to "Item:Q2049099"

The following pages link to Numerical solution of the parametric diffusion equation by deep neural networks (Q2049099):

Displaying 43 items.

• Automated design parameter selection for neural networks solving coupled partial differential equations with discontinuities (Q388540) (← links)
• Multilayer perceptrons as function approximators for analytical solutions of the diffusion equation (Q723074) (← links)
• Efficient approximation of solutions of parametric linear transport equations by ReLU DNNs (Q2026114) (← links)
• Model reduction and neural networks for parametric PDEs (Q2050400) (← links)
• CAS4DL: Christoffel adaptive sampling for function approximation via deep learning (Q2098302) (← links)
• Solving parametric partial differential equations with deep rectified quadratic unit neural networks (Q2103467) (← links)
• A theoretical analysis of deep neural networks and parametric PDEs (Q2117329) (← links)
• Self-adaptive deep neural network: numerical approximation to functions and PDEs (Q2133768) (← links)
• Solving multiscale steady radiative transfer equation using neural networks with uniform stability (Q2157930) (← links)
• Deep neural networks based temporal-difference methods for high-dimensional parabolic partial differential equations (Q2168314) (← links)
• Deep neural network approach to forward-inverse problems (Q2197226) (← links)
• Neural-net-induced Gaussian process regression for function approximation and PDE solution (Q2214653) (← links)
• ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains (Q2222287) (← links)
• Physics informed by deep learning: numerical solutions of modified Korteweg-de Vries equation (Q2244291) (← links)
• Variational Monte Carlo -- bridging concepts of machine learning and high-dimensional partial differential equations (Q2305540) (← links)
• A finite element based deep learning solver for parametric PDEs (Q2670366) (← links)
• Numerical wave propagation aided by deep learning (Q2683049) (← links)
• Long-time integration of parametric evolution equations with physics-informed DeepONets (Q2683074) (← links)
• Computation and learning in high dimensions. Abstracts from the workshop held August 1--7, 2021 (hybrid meeting) (Q2693017) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• The Random Feature Model for Input-Output Maps between Banach Spaces (Q3382802) (← links)
• Emulated digital CNN-UM solution of partial differential equations (Q3425477) (← links)
• A QMC-Deep Learning Method for Diffusivity Estimation in Random Domains (Q4996838) (← links)
• Convergence bounds for empirical nonlinear least-squares (Q5034774) (← links)
• A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Q5058646) (← links)
• Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification (Q5097855) (← links)
• Simultaneous neural network approximation for smooth functions (Q6052416) (← links)
• Numerical solution of nonlinear stochastic differential equations with fractional Brownian motion using fractional-order Genocchi deep neural networks (Q6058729) (← links)
• Deep ReLU neural network approximation in Bochner spaces and applications to parametric PDEs (Q6062166) (← links)
• An introduction to the mathematics of deep learning (Q6064555) (← links)
• Mesh-informed neural networks for operator learning in finite element spaces (Q6077303) (← links)
• A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs (Q6097873) (← links)
• Prediction of numerical homogenization using deep learning for the Richards equation (Q6098948) (← links)
• Exponential Convergence of Deep Operator Networks for Elliptic Partial Differential Equations (Q6108133) (← links)
• Multigrid-Augmented Deep Learning Preconditioners for the Helmholtz Equation (Q6108146) (← links)
• Collocation approximation by deep neural ReLU networks for parametric and stochastic PDEs with lognormal inputs (Q6148127) (← links)
• Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps (Q6153912) (← links)
• The mathematics of artificial intelligence (Q6200206) (← links)
• Error assessment of an adaptive finite elements -- neural networks method for an elliptic parametric PDE (Q6202970) (← links)
• Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations (Q6402542) (← links)
• Learning quantities of interest from parametric PDEs: an efficient neural-weighted minimal residual approach (Q6543646) (← links)
• Operator learning using random features: a tool for scientific computing (Q6585281) (← links)
• One-shot learning of surrogates in PDE-constrained optimization under uncertainty (Q6587616) (← links)