Pages that link to "Item:Q4958918"

The following pages link to Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks (Q4958918):

Displaying 50 items.

• Interpretable machine learning: fundamental principles and 10 grand challenges (Q2074414) (← links)
• CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← links)
• Physics-informed neural networks for shell structures (Q2102673) (← links)
• Physics-informed neural networks for gravity field modeling of small bodies (Q2104214) (← links)
• Prediction of the number of solitons for initial value of nonlinear Schrödinger equation based on the deep learning method (Q2107244) (← links)
• Uniform convergence guarantees for the deep Ritz method for nonlinear problems (Q2110466) (← links)
• Self-adaptive physics-informed neural networks (Q2112437) (← links)
• Neural eikonal solver: improving accuracy of physics-informed neural networks for solving eikonal equation in case of caustics (Q2112483) (← links)
• Hybrid FEM-NN models: combining artificial neural networks with the finite element method (Q2133536) (← links)
• Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs (Q2133772) (← links)
• Simple computational strategies for more effective physics-informed neural networks modeling of turbulent natural convection (Q2133780) (← links)
• When and why PINNs fail to train: a neural tangent kernel perspective (Q2136450) (← links)
• Physics-informed neural networks for gravity field modeling of the Earth and Moon (Q2138489) (← links)
• Meta-learning PINN loss functions (Q2139042) (← links)
• Physics-informed neural networks for rarefied-gas dynamics: Poiseuille flow in the BGK approximation (Q2144364) (← links)
• Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space (Q2145130) (← 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)
• Physics-informed neural networks for inverse problems in supersonic flows (Q2157127) (← links)
• Solving multiscale steady radiative transfer equation using neural networks with uniform stability (Q2157930) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• RPINNs: rectified-physics informed neural networks for solving stationary partial differential equations (Q2166581) (← links)
• Data-driven rogue waves and parameters discovery in nearly integrable \(\mathcal{PT}\)-symmetric Gross-Pitaevskii equations via PINNs deep learning (Q2167994) (← links)
• Data-driven vector soliton solutions of coupled nonlinear Schrödinger equation using a deep learning algorithm (Q2246919) (← links)
• A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335) (← links)
• Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion (Q2671417) (← links)
• Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
• A-WPINN algorithm for the data-driven vector-soliton solutions and parameter discovery of general coupled nonlinear equations (Q2677793) (← links)
• Kolmogorov n-width and Lagrangian physics-informed neural networks: a causality-conforming manifold for convection-dominated PDEs (Q2678525) (← links)
• A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks (Q2679440) (← links)
• Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← 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)
• Isogeometric neural networks: a new deep learning approach for solving parameterized partial differential equations (Q2683423) (← links)
• Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
• Solving free-surface problems for non-shallow water using boundary and initial conditions-free physics-informed neural network (bif-PINN) (Q2687566) (← links)
• An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator (Q2693426) (← links)
• Physics-informed neural networks based on adaptive weighted loss functions for Hamilton-Jacobi equations (Q2694112) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Physics informed neural networks: a case study for gas transport problems (Q2699348) (← links)
• Active Neuron Least Squares: A Training Method for Multivariate Rectified Neural Networks (Q5095494) (← links)
• Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks (Q5161023) (← links)
• Bayesian differential programming for robust systems identification under uncertainty (Q5161165) (← links)
• A combination of large eddy simulation and physics-informed machine learning to predict pore-scale flow behaviours in fibrous porous media: a case study of transient flow passing through a surgical mask (Q6043958) (← links)
• A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics (Q6044222) (← links)
• A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions (Q6048429) (← links)
• Physics-informed neural networks for 2nd order ODEs with sharp gradients (Q6049309) (← links)
• One-dimensional ice shelf hardness inversion: clustering behavior and collocation resampling in physics-informed neural networks (Q6054214) (← links)
• Physics-Informed Neural Networks for Solving Dynamic Two-Phase Interface Problems (Q6068803) (← links)
• VC-PINN: variable coefficient physics-informed neural network for forward and inverse problems of PDEs with variable coefficient (Q6069931) (← links)