Pages that link to "Item:Q2112437"
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The following pages link to Self-adaptive physics-informed neural networks (Q2112437):
Displaying 40 items.
- Parallel physics-informed neural networks via domain decomposition (Q2133497) (← links)
- Conditional physics informed neural networks (Q2247060) (← links)
- Physics-informed neural networks for data-driven simulation: advantages, limitations, and opportunities (Q2683126) (← links)
- A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics (Q6044222) (← links)
- Multifidelity deep operator networks for data-driven and physics-informed problems (Q6048427) (← links)
- A hybrid deep neural operator/finite element method for ice-sheet modeling (Q6054205) (← 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 dimension-augmented physics-informed neural network (DaPINN) with high level accuracy and efficiency (Q6095102) (← links)
- Solving Elliptic Problems with Singular Sources Using Singularity Splitting Deep Ritz Method (Q6095431) (← links)
- Artificial neural network solver for time-dependent Fokker-Planck equations (Q6096280) (← links)
- On the use of neural networks for full waveform inversion (Q6096500) (← links)
- Physics-informed neural networks with adaptive localized artificial viscosity (Q6107107) (← links)
- Adaptive task decomposition physics-informed neural networks (Q6120149) (← links)
- Efficient physics-informed neural networks using hash encoding (Q6126546) (← links)
- Loss-attentional physics-informed neural networks (Q6126561) (← links)
- Learning Specialized Activation Functions for Physics-Informed Neural Networks (Q6143615) (← links)
- Adaptive Learning Rate Residual Network Based on Physics-Informed for Solving Partial Differential Equations (Q6173072) (← links)
- Failure-Informed Adaptive Sampling for PINNs (Q6175124) (← links)
- Discontinuity computing using physics-informed neural networks (Q6184289) (← links)
- Spectral operator learning for parametric PDEs without data reliance (Q6194143) (← links)
- A first-order computational algorithm for reaction-diffusion type equations via primal-dual hybrid gradient method (Q6200402) (← links)
- Adaptive loss weighting auxiliary output fPINNs for solving fractional partial integro-differential equations (Q6496475) (← links)
- Anti-derivatives approximator for enhancing physics-informed neural networks (Q6550163) (← links)
- Physics-informed parallel neural networks with self-adaptive loss weighting for the identification of continuous structural systems (Q6557810) (← links)
- Bright-dark rogue wave transition in coupled ab system via the physics-informed neural networks method (Q6574264) (← links)
- Temporal difference learning for high-dimensional PIDEs with jumps (Q6575343) (← links)
- A gradient-enhanced physics-informed neural networks method for the wave equation (Q6583846) (← links)
- Adaptive sampling points based multi-scale residual network for solving partial differential equations (Q6585372) (← links)
- Multilevel domain decomposition-based architectures for physics-informed neural networks (Q6588267) (← links)
- Partitioned neural network approximation for partial differential equations enhanced with Lagrange multipliers and localized loss functions (Q6588333) (← links)
- Failure-informed adaptive sampling for PINNs. II: Combining with re-sampling and subset simulation (Q6593776) (← links)
- Exact enforcement of temporal continuity in sequential physics-informed neural networks (Q6595862) (← links)
- A transfer learning physics-informed deep learning framework for modeling multiple solute dynamics in unsaturated soils (Q6609790) (← 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)
- Efficiently training physics-informed neural networks via anomaly-aware optimization (Q6662394) (← links)
- NeuroSEM: a hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements (Q6663315) (← links)
- Navigating PINNs via maximum residual-based continuous distribution (Q6669778) (← links)
- Annealed adaptive importance sampling method in PINNs for solving high dimensional partial differential equations (Q6670734) (← links)
