Pages that link to "Item:Q2237440"

The following pages link to On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks (Q2237440):

Displaying 50 items.

• Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks (Q2072449) (← links)
• CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← links)
• NH-PINN: neural homogenization-based physics-informed neural network for multiscale problems (Q2083622) (← links)
• Unified regression model in fitting potential energy surfaces for quantum dynamics (Q2084807) (← links)
• Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation (Q2106998) (← links)
• A deep domain decomposition method based on Fourier features (Q2112697) (← links)
• SPINN: sparse, physics-based, and partially interpretable neural networks for PDEs (Q2133032) (← links)
• Solving and learning nonlinear PDEs with Gaussian processes (Q2133484) (← links)
• Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs (Q2133772) (← links)
• Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN (Q2137979) (← links)
• CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144) (← links)
• Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training (Q2145138) (← links)
• Improved architectures and training algorithms for deep operator networks (Q2149522) (← links)
• A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems (Q2222703) (← links)
• Scalable uncertainty quantification for deep operator networks using randomized priors (Q2674111) (← links)
• Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
• Physics-informed neural networks combined with polynomial interpolation to solve nonlinear partial differential equations (Q2682670) (← links)
• Long-time integration of parametric evolution equations with physics-informed DeepONets (Q2683074) (← links)
• Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
• opPINN: physics-informed neural network with operator learning to approximate solutions to the Fokker-Planck-Landau equation (Q2689626) (← links)
• An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator (Q2693426) (← links)
• Deep energy method in topology optimization applications (Q2694685) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Active Neuron Least Squares: A Training Method for Multivariate Rectified Neural Networks (Q5095494) (← links)
• MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs (Q5106291) (← links)
• A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions (Q6048429) (← links)
• Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
• Enhanced physics‐informed neural networks for hyperelasticity (Q6071403) (← links)
• BINN: a deep learning approach for computational mechanics problems based on boundary integral equations (Q6094674) (← links)
• Physics-informed radial basis network (PIRBN): a local approximating neural network for solving nonlinear partial differential equations (Q6096508) (← links)
• Pre-training strategy for solving evolution equations based on physics-informed neural networks (Q6107095) (← links)
• HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions (Q6119293) (← links)
• Respecting causality for training physics-informed neural networks (Q6121791) (← links)
• Efficient physics-informed neural networks using hash encoding (Q6126546) (← links)
• Bayesian Deep Learning Framework for Uncertainty Quantification in Stochastic Partial Differential Equations (Q6154967) (← links)
• Subspace decomposition based DNN algorithm for elliptic type multi-scale PDEs (Q6162913) (← links)
• Exact Dirichlet boundary physics-informed neural network EPINN for solid mechanics (Q6171233) (← links)
• Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations (Q6171723) (← links)
• A Neural Network Approach for Homogenization of Multiscale Problems (Q6178099) (← links)
• On the spectral bias of coupled frequency predictor-corrector triangular DNN: the convergence analysis (Q6179933) (← links)
• Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations (Q6187659) (← links)
• Gradient-enhanced physics-informed neural networks based on transfer learning for inverse problems of the variable coefficient differential equations (Q6191522) (← links)
• Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent? (Q6198233) (← links)
• Physics-informed ConvNet: learning physical field from a shallow neural network (Q6199712) (← links)
• Physical informed neural networks with soft and hard boundary constraints for solving advection-diffusion equations using Fourier expansions (Q6202605) (← links)
• Residual-based attention in physics-informed neural networks (Q6202991) (← links)
• Optimization of physics-informed neural networks for solving the nolinear Schrödinger equation (Q6204256) (← links)
• Constraint free physics-informed machine learning for micromagnetic energy minimization (Q6543808) (← links)
• Dynamically configured physics-informed neural network in topology optimization applications (Q6550166) (← links)
• Neural-integrated meshfree (NIM) method: a differentiable programming-based hybrid solver for computational mechanics (Q6557785) (← links)