Pages that link to "Item:Q2021893"
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The following pages link to A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (Q2021893):
Displaying 34 items.
- An introduction to programming physics-informed neural network-based computational solid mechanics (Q6564385) (← links)
- Theory and implementation of inelastic constitutive artificial neural networks (Q6566033) (← links)
- Physical informed neural network for thermo-hydral analysis of fire-loaded concrete (Q6566857) (← links)
- Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains (Q6569914) (← links)
- PINN enhanced extended multiscale finite element method for fast mechanical analysis of heterogeneous materials (Q6576389) (← links)
- Data-driven physics-constrained recurrent neural networks for multiscale damage modeling of metallic alloys with process-induced porosity (Q6584871) (← links)
- Phase-field modeling of fracture with physics-informed deep learning (Q6588261) (← links)
- A robust radial point interpolation method empowered with neural network solvers (RPIM-NNS) for nonlinear solid mechanics (Q6588318) (← links)
- Inf-sup neural networks for high-dimensional elliptic PDE problems (Q6589859) (← links)
- Phase field smoothing-PINN: a neural network solver for partial differential equations with discontinuous coefficients (Q6590262) (← links)
- E-PINN: extended physics informed neural network for the forward and inverse problems of high-order nonlinear integro-differential equations (Q6590587) (← links)
- Machine learning for nonlinear integro-differential equations with degenerate kernel scheme (Q6591000) (← links)
- Solving American option optimal control problems in financial markets using a novel neural network (Q6593226) (← links)
- Physics-informed deep learning of rate-and-state fault friction (Q6595877) (← links)
- Transfer learning enhanced nonlocal energy-informed neural network for quasi-static fracture in rock-like materials (Q6595896) (← links)
- Pseudo grid-based physics-informed convolutional-recurrent network solving the integrable nonlinear lattice equations (Q6599876) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks (Q6604129) (← links)
- Interpretable physics-encoded finite element network to handle concentration features and multi-material heterogeneity in hyperelasticity (Q6609781) (← links)
- f-PICNN: a physics-informed convolutional neural network for partial differential equations with space-time domain (Q6614990) (← links)
- On physics-informed neural networks training for coupled hydro-poromechanical problems (Q6615008) (← links)
- Physics-informed machine learning for the inverse design of wave scattering clusters (Q6632907) (← links)
- Higher-order multi-scale physics-informed neural network (HOMS-PINN) method and its convergence analysis for solving elastic problems of authentic composite materials (Q6633295) (← links)
- PDE generalization of in-context operator networks: a study on 1D scalar nonlinear conservation laws (Q6639294) (← links)
- Ensemble of physics-informed neural networks for solving plane elasticity problems with examples (Q6639905) (← links)
- Physics informed self-supervised segmentation of elastic composite materials (Q6641894) (← links)
- Physics-informed holomorphic neural networks (PIHNNs): solving 2D linear elasticity problems (Q6643566) (← links)
- Variational Bayesian surrogate modelling with application to robust design optimisation (Q6643588) (← 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)
- Simple yet effective adaptive activation functions for physics-informed neural networks (Q6660250) (← links)
- Finite element-integrated neural network framework for elastic and elastoplastic solids (Q6663275) (← links)
- NeuroSEM: a hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements (Q6663315) (← links)
- Taylor series error correction network for super-resolution of discretized partial differential equation solutions (Q6669099) (← links)
- Combining physics-informed graph neural network and finite difference for solving forward and inverse spatiotemporal PDEs (Q6671950) (← links)
