Pages that link to "Item:Q2236167"

The following pages link to Physics-informed multi-LSTM networks for metamodeling of nonlinear structures (Q2236167):

Displaying 30 items.

• Deep generative modeling for mechanistic-based learning and design of metamaterial systems (Q2020976) (← links)
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (Q2021893) (← links)
• Data-driven simulation for general-purpose multibody dynamics using deep neural networks (Q2034108) (← links)
• PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
• Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems (Q2072742) (← links)
• Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response (Q2096901) (← links)
• PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (Q2128357) (← links)
• CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• Surrogate modeling of elasto-plastic problems via long short-term memory neural networks and proper orthogonal decomposition (Q2237770) (← links)
• Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
• FEA-Net: a physics-guided data-driven model for efficient mechanical response prediction (Q2309378) (← links)
• A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335) (← 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)
• An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator (Q2693426) (← links)
• Physically motivated structuring and optimization of neural networks for multi-physics modelling of solid oxide fuel cells (Q5070708) (← links)
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• PhySR: physics-informed deep super-resolution for spatiotemporal data (Q6054215) (← links)
• Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems (Q6119307) (← links)
• SeismicNET: physics-informed neural networks for seismic wave modeling in semi-infinite domain (Q6137634) (← links)
• Transfer learning of recurrent neural network‐based plasticity models (Q6148497) (← links)
• Embedding physical knowledge in deep neural networks for predicting the phonon dispersion curves of cellular metamaterials (Q6159334) (← links)
• Bayesian synergistic metamodeling (BSM) for physical information infused data-driven metamodeling (Q6185242) (← links)
• Neural-integrated meshfree (NIM) method: a differentiable programming-based hybrid solver for computational mechanics (Q6557785) (← links)
• Probabilistic graph networks for learning physics simulations (Q6572165) (← links)
• Deep learning in computational mechanics: a review (Q6604128) (← 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)
• Multi-fidelity enhanced few-shot time series prediction model for structural dynamics analysis (Q6669072) (← links)
• Combining physics-informed graph neural network and finite difference for solving forward and inverse spatiotemporal PDEs (Q6671950) (← links)