Pages that link to "Item:Q2671403"
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The following pages link to On computing the hyperparameter of extreme learning machines: algorithm and application to computational PDEs, and comparison with classical and high-order finite elements (Q2671403):
Displaying 12 items.
- Numerical approximation of partial differential equations by a variable projection method with artificial neural networks (Q2160472) (← links)
- Machine learning based refinement strategies for polyhedral grids with applications to virtual element and polyhedral discontinuous Galerkin methods (Q2675603) (← links)
- Data-driven control of agent-based models: an equation/variable-free machine learning approach (Q2687520) (← links)
- Physically informed deep homogenization neural network for unidirectional multiphase/multi-inclusion thermoconductive composites (Q6101900) (← links)
- A method for computing inverse parametric PDE problems with random-weight neural networks (Q6107102) (← links)
- Numerical computation of partial differential equations by hidden-layer concatenated extreme learning machine (Q6159015) (← links)
- Local randomized neural networks with discontinuous Galerkin methods for diffusive-viscous wave equation (Q6184721) (← links)
- A new numerical approach method to solve the Lotka-Volterra predator-prey models with discrete delays (Q6197526) (← links)
- On Computing the Hyperparameter of Extreme Learning Machines: Algorithm and Application to Computational PDEs, and Comparison with Classical and High-Order Finite Elements (Q6381389) (← links)
- A Chebyshev neural network-based numerical scheme to solve distributed-order fractional differential equations (Q6543647) (← links)
- Slow invariant manifolds of singularly perturbed systems via physics-informed machine learning (Q6573172) (← links)
- A new high-precision numerical method for solving the HIV infection model of CD4(+) cells (Q6622880) (← links)
