The following pages link to PINNsNTK (Q1352924):
Displaying 28 items.
- A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (Q2021893) (← links)
- Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
- A physics-informed multi-fidelity approach for the estimation of differential equations parameters in low-data or large-noise regimes (Q2075654) (← links)
- An efficient neural network method with plane wave activation functions for solving Helmholtz equation (Q2122592) (← links)
- Structure probing neural network deflation (Q2124019) (← links)
- Solving and learning nonlinear PDEs with Gaussian processes (Q2133484) (← 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)
- Physics-informed neural networks for the shallow-water equations on the sphere (Q2133783) (← links)
- On quadrature rules for solving partial differential equations using neural networks (Q2138756) (← links)
- A general neural particle method for hydrodynamics modeling (Q2138776) (← links)
- Physics informed neural networks for continuum micromechanics (Q2138812) (← links)
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems (Q2138842) (← 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)
- Numerical approximation of partial differential equations by a variable projection method with artificial neural networks (Q2160472) (← links)
- Physics-informed neural networks for learning the homogenized coefficients of multiscale elliptic equations (Q2162011) (← links)
- Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
- Data-driven rogue waves and parameters discovery in nearly integrable \(\mathcal{PT}\)-symmetric Gross-Pitaevskii equations via PINNs deep learning (Q2167994) (← links)
- On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks (Q2237440) (← links)
- Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems (Q2671323) (← links)
- A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335) (← links)
- Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks (Q2671386) (← links)
- On computing the hyperparameter of extreme learning machines: algorithm and application to computational PDEs, and comparison with classical and high-order finite elements (Q2671403) (← links)
- ModalPINN: an extension of physics-informed neural networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors (Q2672754) (← links)
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks (Q4958918) (← links)
- High Order Deep Neural Network for Solving High Frequency Partial Differential Equations (Q5065177) (← links)
