Pages that link to "Item:Q2021164"
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The following pages link to The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics (Q2021164):
Displaying 36 items.
- Constraint-aware neural networks for Riemann problems (Q778316) (← links)
- PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
- Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics (Q2083129) (← links)
- A robust unsupervised neural network framework for geometrically nonlinear analysis of inelastic truss structures (Q2109538) (← links)
- A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media (Q2132604) (← links)
- A general neural particle method for hydrodynamics modeling (Q2138776) (← links)
- Physics informed neural networks for continuum micromechanics (Q2138812) (← links)
- RPINNs: rectified-physics informed neural networks for solving stationary partial differential equations (Q2166581) (← links)
- Parametric deep energy approach for elasticity accounting for strain gradient effects (Q2246296) (← links)
- ReF-nets: physics-informed neural network for Reynolds equation of gas bearing (Q2670343) (← links)
- Structure preservation for the deep neural network multigrid solver (Q2672194) (← 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 for data-driven simulation: advantages, limitations, and opportunities (Q2683126) (← links)
- Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
- Solving free-surface problems for non-shallow water using boundary and initial conditions-free physics-informed neural network (bif-PINN) (Q2687566) (← links)
- Deep energy method in topology optimization applications (Q2694685) (← links)
- Computational Homogenization Using Convolutional Neural Networks (Q5051078) (← links)
- A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics (Q6044222) (← links)
- A surrogate model for the prediction of permeabilities and flow through porous media: a machine learning approach based on stochastic Brownian motion (Q6044223) (← links)
- Transformers for modeling physical systems (Q6055222) (← links)
- Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
- A stepwise physics‐informed neural network for solving large deformation problems of hypoelastic materials (Q6082603) (← links)
- Physics-informed radial basis network (PIRBN): a local approximating neural network for solving nonlinear partial differential equations (Q6096508) (← links)
- Neural vortex method: from finite Lagrangian particles to infinite dimensional Eulerian dynamics (Q6100103) (← links)
- Deep convolutional Ritz method: parametric PDE surrogates without labeled data (Q6132294) (← links)
- Efficient multiscale modeling of heterogeneous materials using deep neural networks (Q6159331) (← links)
- The coupled physical-informed neural networks for the two phase magnetohydrodynamic flows (Q6549888) (← links)
- An introduction to programming physics-informed neural network-based computational solid mechanics (Q6564385) (← links)
- A consistent second order ISPH for free surface flow (Q6566933) (← links)
- A robust radial point interpolation method empowered with neural network solvers (RPIM-NNS) for nonlinear solid mechanics (Q6588318) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- A neural particle method with interface tracking and adaptive particle refinement for free surface flows (Q6646467) (← links)
- Prediction of spatiotemporal dynamics using deep learning: coupled neural networks of long short-terms memory, auto-encoder and physics-informed neural networks (Q6650113) (← links)
- Differentiability in unrolled training of neural physics simulators on transient dynamics (Q6663245) (← links)
- NeuroSEM: a hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements (Q6663315) (← links)
