Pages that link to "Item:Q2671335"
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The following pages link to A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335):
Displaying 33 items.
- Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
- Time difference physics-informed neural network for fractional water wave models (Q2690093) (← links)
- An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
- An efficient numerical algorithm for solving nonlinear Volterra integral equations in the reproducing kernel space (Q6046879) (← links)
- A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions (Q6048429) (← links)
- Enforcing continuous symmetries in physics-informed neural network for solving forward and inverse problems of partial differential equations (Q6078492) (← links)
- Prediction of numerical homogenization using deep learning for the Richards equation (Q6098948) (← links)
- A decoupled physics-informed neural network for recovering a space-dependent force function in the wave equation from integral overdetermination data (Q6103366) (← links)
- A method for computing inverse parametric PDE problems with random-weight neural networks (Q6107102) (← links)
- NAS-PINN: neural architecture search-guided physics-informed neural network for solving PDEs (Q6117703) (← links)
- Physics informed WNO (Q6120131) (← links)
- A novel approach for solving linear Fredholm integro-differential equations via LS-SVM algorithm (Q6130113) (← links)
- Pre-training physics-informed neural network with mixed sampling and its application in high-dimensional systems (Q6130985) (← links)
- MC-Nonlocal-PINNs: Handling Nonlocal Operators in PINNs Via Monte Carlo Sampling (Q6151271) (← links)
- Adaptive transfer learning for PINN (Q6173323) (← links)
- Multi-output physics-informed neural network for one- and two-dimensional nonlinear time distributed-order models (Q6186170) (← links)
- Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions (Q6204733) (← links)
- Adaptive loss weighting auxiliary output fPINNs for solving fractional partial integro-differential equations (Q6496475) (← links)
- Zero coordinate shift: whetted automatic differentiation for physics-informed operator learning (Q6497254) (← links)
- Data-driven fusion and fission solutions in the Hirota-Satsuma-Ito equation via the physics-informed neural networks method (Q6497884) (← links)
- A deep learning method for computing mean exit time excited by weak Gaussian noise (Q6539426) (← links)
- An accurate RBF-based meshless technique for the inverse multi-term time-fractional integro-differential equation (Q6539766) (← links)
- Solving the non-local Fokker-Planck equations by deep learning (Q6551384) (← links)
- Physical informed neural network for thermo-hydral analysis of fire-loaded concrete (Q6566857) (← links)
- Reconstructing \(S\)-matrix phases with machine learning (Q6568203) (← links)
- Solving higher-order nonlinear Volterra integro-differential equations using two discretization methods (Q6586101) (← links)
- Phase field smoothing-PINN: a neural network solver for partial differential equations with discontinuous coefficients (Q6590262) (← links)
- E-PINN: extended physics informed neural network for the forward and inverse problems of high-order nonlinear integro-differential equations (Q6590587) (← links)
- Machine learning for nonlinear integro-differential equations with degenerate kernel scheme (Q6591000) (← links)
- Prediction of discretization of online GMsFEM using deep learning for Richards equation (Q6593325) (← links)
- Numerical solutions of KdV and mKDV equations: using sequence and multi-core parallelization implementation (Q6593335) (← links)
- Physics-specialized neural network with hard constraints for solving multi-material diffusion problems (Q6595894) (← links)
- Multistep asymptotic pre-training strategy based on PINNs for solving steep boundary singular perturbation problems (Q6609750) (← links)
