Pages that link to "Item:Q1988348"

The following pages link to Deep neural networks motivated by partial differential equations (Q1988348):

Displaying 50 items.

• Translating numerical concepts for PDEs into neural architectures (Q826193) (← links)
• Model reduction and neural networks for parametric PDEs (Q2050400) (← links)
• Numerical solving of nonlinear differential equations using a hybrid method on a semi-infinite interval (Q2052338) (← links)
• Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs (Q2060092) (← links)
• Some open questions on morphological operators and representations in the deep learning era. A personal vision (Q2061783) (← links)
• Quantized convolutional neural networks through the lens of partial differential equations (Q2079526) (← links)
• Fully hyperbolic convolutional neural networks (Q2079530) (← links)
• Learning phase field mean curvature flows with neural networks (Q2083658) (← links)
• Deep learning schemes for parabolic nonlocal integro-differential equations (Q2098092) (← links)
• Data-driven deep learning of partial differential equations in modal space (Q2123370) (← links)
• Using neural networks to accelerate the solution of the Boltzmann equation (Q2132591) (← links)
• Self-adaptive deep neural network: numerical approximation to functions and PDEs (Q2133768) (← links)
• Deep microlocal reconstruction for limited-angle tomography (Q2134111) (← links)
• On quadrature rules for solving partial differential equations using neural networks (Q2138756) (← links)
• Mean-field and kinetic descriptions of neural differential equations (Q2148968) (← links)
• Modelling spatiotemporal dynamics from Earth observation data with neural differential equations (Q2163266) (← links)
• Deep neural networks based temporal-difference methods for high-dimensional parabolic partial differential equations (Q2168314) (← links)
• Designing rotationally invariant neural networks from PDEs and variational methods (Q2168880) (← links)
• Discrete processes and their continuous limits (Q2197184) (← links)
• A review on deep learning in medical image reconstruction (Q2218098) (← links)
• Deep learning methods for the computation of vibrational wavefunctions (Q2246978) (← links)
• Structure preservation for the deep neural network multigrid solver (Q2672194) (← links)
• Deep limits of residual neural networks (Q2679108) (← links)
• Learning high-dimensional parametric maps via reduced basis adaptive residual networks (Q2679335) (← links)
• A deep Fourier residual method for solving PDEs using neural networks (Q2683430) (← links)
• Optimal control by deep learning techniques and its applications on epidemic models (Q2684035) (← links)
• Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning (Q2688065) (← links)
• Convex and concave envelopes of artificial neural network activation functions for deterministic global optimization (Q2689856) (← links)
• Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← 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)
• Bilevel optimization, deep learning and fractional Laplacian regularization with applications in tomography (Q5000616) (← links)
• Structure-preserving deep learning (Q5014474) (← links)
• A New Artificial Neural Network Method for Solving Schrödinger Equations on Unbounded Domains (Q5045673) (← links)
• Wasserstein-Based Projections with Applications to Inverse Problems (Q5074785) (← links)
• Approximations with deep neural networks in Sobolev time-space (Q5075578) (← links)
• slimTrain---A Stochastic Approximation Method for Training Separable Deep Neural Networks (Q5095499) (← links)
• Deep Neural Networks and PIDE Discretizations (Q5100094) (← links)
• Convolutional Neural Networks in Phase Space and Inverse Problems (Q5149211) (← links)
• PNKH-B: A Projected Newton--Krylov Method for Large-Scale Bound-Constrained Optimization (Q5161766) (← links)
• Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection (Q5162626) (← links)
• An Efficient Parallel-in-Time Method for Optimization with Parabolic PDEs (Q5208718) (← links)
• Scale-covariant and scale-invariant Gaussian derivative networks (Q5918660) (← links)
• Strong stationarity for optimal control problems with non-smooth integral equation constraints: application to a continuous DNN (Q6058518) (← links)
• DEEP EQUILIBRIUM NETS (Q6067145) (← links)
• Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
• Model discovery of compartmental models with graph-supported neural networks (Q6090296) (← links)
• Accuracy and architecture studies of residual neural network method for ordinary differential equations (Q6101548) (← links)
• Pseudo-Hamiltonian neural networks for learning partial differential equations (Q6119277) (← links)
• An ODE-based neural network with Bayesian optimization (Q6139497) (← links)