Pages that link to "Item:Q2068635"
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The following pages link to Optimally weighted loss functions for solving PDEs with neural networks (Q2068635):
Displaying 24 items.
- On the antiderivatives of \(x^p/(1 - x)\) with an application to optimize loss functions for classification with neural networks (Q2122774) (← links)
- Lagrangian dual framework for conservative neural network solutions of kinetic equations (Q2158858) (← links)
- RPINNs: rectified-physics informed neural networks for solving stationary partial differential equations (Q2166581) (← links)
- Adaptive activation functions accelerate convergence in deep and physics-informed neural networks (Q2223034) (← links)
- Deep relaxation: partial differential equations for optimizing deep neural networks (Q2319762) (← links)
- Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion (Q2671417) (← links)
- Neural control of discrete weak formulations: Galerkin, least squares \& minimal-residual methods with quasi-optimal weights (Q2679332) (← links)
- Physics-informed neural networks combined with polynomial interpolation to solve nonlinear partial differential equations (Q2682670) (← links)
- Physics-informed neural networks based on adaptive weighted loss functions for Hamilton-Jacobi equations (Q2694112) (← links)
- Physics informed neural networks: a case study for gas transport problems (Q2699348) (← links)
- Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)
- On a multilevel Levenberg–Marquardt method for the training of artificial neural networks and its application to the solution of partial differential equations (Q5038185) (← links)
- One-dimensional ice shelf hardness inversion: clustering behavior and collocation resampling in physics-informed neural networks (Q6054214) (← links)
- Physics-informed neural networks with parameter asymptotic strategy for learning singularly perturbed convection-dominated problem (Q6062204) (← links)
- Some elliptic second order problems and neural network solutions: existence and error estimates (Q6073185) (← links)
- Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems (Q6095075) (← links)
- A decoupled physics-informed neural network for recovering a space-dependent force function in the wave equation from integral overdetermination data (Q6103366) (← links)
- Investigating and Mitigating Failure Modes in Physics-Informed Neural Networks (PINNs) (Q6111307) (← links)
- Variable separated physics-informed neural networks based on adaptive weighted loss functions for blood flow model (Q6144182) (← links)
- Boundary-safe PINNs extension: application to non-linear parabolic PDEs in counterparty credit risk (Q6157931) (← links)
- Adaptive transfer learning for PINN (Q6173323) (← links)
- Optimally weighted loss functions for solving PDEs with Neural Networks (Q6334842) (← links)
- High-dimensional stochastic control models for newsvendor problems and deep learning resolution (Q6589107) (← links)
- Recent developments in machine learning methods for stochastic control and games (Q6615618) (← links)
