Pages that link to "Item:Q2133497"
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The following pages link to Parallel physics-informed neural networks via domain decomposition (Q2133497):
Displaying 50 items.
- CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← links)
- INN: interfaced neural networks as an accessible meshless approach for solving interface PDE problems (Q2083675) (← links)
- Data-driven prediction of soliton solutions of the higher-order NLSE via the strongly-constrained PINN method (Q2107164) (← links)
- GINNs: graph-informed neural networks for multiscale physics (Q2120776) (← links)
- Physics-informed neural networks for the shallow-water equations on the sphere (Q2133783) (← links)
- A general neural particle method for hydrodynamics modeling (Q2138776) (← links)
- Meta-learning PINN loss functions (Q2139042) (← links)
- Physics-informed neural networks for inverse problems in supersonic flows (Q2157127) (← links)
- HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations (Q2172562) (← links)
- Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
- A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks (Q2679440) (← links)
- Physics-informed neural networks combined with polynomial interpolation to solve nonlinear partial differential equations (Q2682670) (← links)
- Physics-informed neural networks for data-driven simulation: advantages, limitations, and opportunities (Q2683126) (← links)
- An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator (Q2693426) (← links)
- A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions (Q6048429) (← links)
- Dynamic analysis on optical pulses via modified PINNs: soliton solutions, rogue waves and parameter discovery of the CQ-NLSE (Q6058699) (← links)
- Physics-informed neural networks with parameter asymptotic strategy for learning singularly perturbed convection-dominated problem (Q6062204) (← 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)
- A framework based on symbolic regression coupled with eXtended physics-informed neural networks for gray-box learning of equations of motion from data (Q6096490) (← links)
- Physics-informed radial basis network (PIRBN): a local approximating neural network for solving nonlinear partial differential equations (Q6096508) (← links)
- Solving multi-material problems in solid mechanics using physics-informed neural networks based on domain decomposition technology (Q6099225) (← links)
- Detecting stochastic governing laws with observation on stationary distributions (Q6102440) (← links)
- Pre-training physics-informed neural network with mixed sampling and its application in high-dimensional systems (Q6130985) (← links)
- Learning Specialized Activation Functions for Physics-Informed Neural Networks (Q6143615) (← links)
- Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations (Q6171723) (← links)
- Variationally mimetic operator networks (Q6185143) (← links)
- wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws (Q6197777) (← 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)
- Anti-derivatives approximator for enhancing physics-informed neural networks (Q6550163) (← links)
- Adaptive deep Fourier residual method via overlapping domain decomposition (Q6557761) (← links)
- Neural-integrated meshfree (NIM) method: a differentiable programming-based hybrid solver for computational mechanics (Q6557785) (← links)
- Physics-informed parallel neural networks with self-adaptive loss weighting for the identification of continuous structural systems (Q6557810) (← links)
- Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains (Q6569914) (← links)
- Bright-dark rogue wave transition in coupled ab system via the physics-informed neural networks method (Q6574264) (← links)
- Adaptive sampling points based multi-scale residual network for solving partial differential equations (Q6585372) (← links)
- Partitioned neural network approximation for partial differential equations enhanced with Lagrange multipliers and localized loss functions (Q6588333) (← links)
- A meshless solver for blood flow simulations in elastic vessels using a physics-informed neural network (Q6590130) (← links)
- Iterative algorithms for partitioned neural network approximation to partial differential equations (Q6590244) (← links)
- Advanced physics-informed neural networks for numerical approximation of the coupled Schrödinger-KdV equation (Q6590978) (← links)
- A domain decomposition-based CNN-DNN architecture for model parallel training applied to image recognition problems (Q6598498) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Multistep asymptotic pre-training strategy based on PINNs for solving steep boundary singular perturbation problems (Q6609750) (← links)
- A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks (Q6609808) (← links)
- f-PICNN: a physics-informed convolutional neural network for partial differential equations with space-time domain (Q6614990) (← links)
- Enhancing training of physics-informed neural networks using domain decomposition-based preconditioning strategies (Q6623675) (← links)
- Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of a time fractional equation (Q6630929) (← links)
- Physics-informed holomorphic neural networks (PIHNNs): solving 2D linear elasticity problems (Q6643566) (← links)
- Prediction of spatiotemporal dynamics using deep learning: coupled neural networks of long short-terms memory, auto-encoder and physics-informed neural networks (Q6650113) (← links)
