Pages that link to "Item:Q2125428"
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The following pages link to Trend to equilibrium for the kinetic Fokker-Planck equation via the neural network approach (Q2125428):
Displaying 14 items.
- Self-similar characteristics of neural networks based on Fokker-Planck equation (Q1878048) (← links)
- Traveling wave solutions of partial differential equations via neural networks (Q1983171) (← links)
- Neural network approach to data-driven estimation of chemotactic sensitivity in the Keller-Segel model (Q2092227) (← links)
- Solving multiscale steady radiative transfer equation using neural networks with uniform stability (Q2157930) (← links)
- Lagrangian dual framework for conservative neural network solutions of kinetic equations (Q2158858) (← links)
- opPINN: physics-informed neural network with operator learning to approximate solutions to the Fokker-Planck-Landau equation (Q2689626) (← links)
- The model reduction of the Vlasov–Poisson–Fokker–Planck system to the Poisson–Nernst–Planck system <i>via</i> the Deep Neural Network Approach (Q5163496) (← links)
- The deep minimizing movement scheme (Q6087937) (← links)
- Capturing the diffusive behavior of the multiscale linear transport equations by asymptotic-preserving convolutional deeponets (Q6118592) (← links)
- Asymptotic-preserving neural networks for multiscale time-dependent linear transport equations (Q6158979) (← links)
- Trend to Equilibrium for the Kinetic Fokker-Planck Equation via the Neural Network Approach (Q6329679) (← links)
- Data driven adaptive Gaussian mixture model for solving Fokker-Planck equation (Q6560605) (← links)
- A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations (Q6584818) (← links)
- Asymptotic-preserving neural networks for multiscale kinetic equations (Q6585905) (← links)
