Pages that link to "Item:Q2222362"
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The following pages link to Data driven governing equations approximation using deep neural networks (Q2222362):
Displaying 26 items.
- Deep-OSG: deep learning of operators in semigroup (Q6094763) (← links)
- DNN modeling of partial differential equations with incomplete data (Q6094765) (← links)
- Accuracy and architecture studies of residual neural network method for ordinary differential equations (Q6101548) (← links)
- Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data (Q6110192) (← links)
- Structure-preserving recurrent neural networks for a class of Birkhoffian systems (Q6130981) (← links)
- A Review of Data‐Driven Discovery for Dynamic Systems (Q6131430) (← links)
- A Variational Neural Network Approach for Glacier Modelling with Nonlinear Rheology (Q6143617) (← links)
- Quadrature rule based discovery of dynamics by data-driven denoising (Q6147082) (← links)
- On the identification and optimization of nonsmooth superposition operators in semilinear elliptic PDEs (Q6151942) (← links)
- SPADE4: sparsity and delay embedding based forecasting of epidemics (Q6168035) (← links)
- Deep neural network based adaptive learning for switched systems (Q6172098) (← links)
- A learned conservative semi-Lagrangian finite volume scheme for transport simulations (Q6173366) (← links)
- Learning the Dynamics for Unknown Hyperbolic Conservation Laws Using Deep Neural Networks (Q6195013) (← links)
- On mathematical modeling in image reconstruction and beyond (Q6200218) (← links)
- Data-driven modeling of partially observed biological systems (Q6537200) (← links)
- A data-driven framework for learning hybrid dynamical systems (Q6548679) (← links)
- Learning nonparametric ordinary differential equations from noisy data (Q6553789) (← links)
- Data-driven models of nonautonomous systems (Q6553794) (← links)
- Model-free inference of unseen attractors: reconstructing phase space features from a single noisy trajectory using Reservoir computing (Q6557728) (← links)
- Learning particle swarming models from data with Gaussian processes (Q6562843) (← links)
- A causality-DeepONet for causal responses of linear dynamical systems (Q6584819) (← links)
- Machine learning approaches for the solution of the Riemann problem in fluid dynamics: a case study (Q6593781) (← links)
- Error analysis based on inverse modified differential equations for discovery of dynamics using linear multistep methods and deep learning (Q6601198) (← links)
- Simulating partial differential equations with neural networks (Q6613539) (← links)
- On the identifiability of nonlocal interaction kernels in first-order systems of interacting particles on Riemannian manifolds (Q6620727) (← links)
- Machine learning methods for reduced order modeling (Q6629175) (← links)
