Pages that link to "Item:Q2136443"
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The following pages link to Thermodynamically consistent physics-informed neural networks for hyperbolic systems (Q2136443):
Displaying 26 items.
- Neural-network based collision operators for the Boltzmann equation (Q2083624) (← links)
- A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method (Q2096848) (← links)
- Physics-informed neural networks for inverse problems in supersonic flows (Q2157127) (← links)
- Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
- Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
- A molecular-continuum multiscale model for inviscid liquid-vapor flow with sharp interfaces (Q2675620) (← links)
- Kolmogorov n-width and Lagrangian physics-informed neural networks: a causality-conforming manifold for convection-dominated PDEs (Q2678525) (← links)
- Intelligent dissipative particle dynamics: bridging mesoscopic models from microscopic simulations via deep neural networks (Q2683077) (← links)
- Physics informed neural networks: a case study for gas transport problems (Q2699348) (← links)
- CoolPINNs: a physics-informed neural network modeling of active cooling in vascular systems (Q6072827) (← links)
- Deep learning data-driven multi-soliton dynamics and parameters discovery for the fifth-order Kaup-Kuperschmidt equation (Q6096544) (← links)
- Deep learning for thermal plasma simulation: solving 1-D arc model as an example (Q6097959) (← links)
- A method for computing inverse parametric PDE problems with random-weight neural networks (Q6107102) (← links)
- Physics-informed neural networks with adaptive localized artificial viscosity (Q6107107) (← links)
- Error estimates and physics informed augmentation of neural networks for thermally coupled incompressible Navier Stokes equations (Q6109270) (← links)
- Investigating and Mitigating Failure Modes in Physics-Informed Neural Networks (PINNs) (Q6111307) (← links)
- HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions (Q6119293) (← links)
- Least-squares neural network (LSNN) method for scalar nonlinear hyperbolic conservation laws: discrete divergence operator (Q6175199) (← links)
- Discontinuity computing using physics-informed neural networks (Q6184289) (← links)
- wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws (Q6197777) (← links)
- Thermodynamically consistent physics-informed neural networks for hyperbolic systems (Q6355686) (← links)
- Lie-Poisson neural networks (LPNets): data-based computing of Hamiltonian systems with symmetries (Q6536105) (← links)
- Deep learning of first-order nonlinear hyperbolic conservation law solvers (Q6560690) (← links)
- Non-diffusive neural network method for hyperbolic conservation laws (Q6572177) (← links)
- Improving weak PINNs for hyperbolic conservation laws: dual norm computation, boundary conditions and systems (Q6658817) (← links)
- Unsupervised neural-network solvers for multi-material Riemann problems (Q6671955) (← links)
