Pages that link to "Item:Q2021893"

The following pages link to A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (Q2021893):

Displaying 50 items.

• Circumventing the solution of inverse problems in mechanics through deep learning: application to elasticity imaging (Q1988119) (← links)
• Non-invasive inference of thrombus material properties with physics-informed neural networks (Q2022055) (← links)
• Data-driven reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation (Q2059122) (← links)
• Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks (Q2060125) (← links)
• PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
• A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization (Q2072746) (← links)
• CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← 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-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response (Q2096901) (← links)
• Physics-informed neural networks for shell structures (Q2102673) (← links)
• Optimal control of PDEs using physics-informed neural networks (Q2106939) (← links)
• A robust unsupervised neural network framework for geometrically nonlinear analysis of inelastic truss structures (Q2109538) (← links)
• Nonlinear input feature reduction for data-based physical modeling (Q2112546) (← links)
• Force density-informed neural network for prestress design of tensegrity structures with multiple self-stress modes (Q2134380) (← links)
• Physics-informed neural networks for gravity field modeling of the Earth and Moon (Q2138489) (← links)
• A general neural particle method for hydrodynamics modeling (Q2138776) (← links)
• Physics informed neural networks for continuum micromechanics (Q2138812) (← links)
• IGA-reuse-NET: a deep-learning-based isogeometric analysis-reuse approach with topology-consistent parameterization (Q2139715) (← links)
• CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144) (← links)
• Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training (Q2145138) (← links)
• Residual-based adaptivity for two-phase flow simulation in porous media using physics-informed neural networks (Q2156788) (← links)
• Physics-informed neural networks for inverse problems in supersonic flows (Q2157127) (← links)
• Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling (Q2160481) (← links)
• Probabilistic deep learning for real-time large deformation simulations (Q2160483) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• Physics-informed PointNet: a deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries (Q2168328) (← links)
• A deep learning-based hybrid approach for the solution of multiphysics problems in electrosurgery (Q2179220) (← links)
• A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches (Q2237330) (← links)
• Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method (Q2237428) (← links)
• Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture (Q2237458) (← links)
• A nonlocal physics-informed deep learning framework using the peridynamic differential operator (Q2237731) (← 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)
• Theory-guided auto-encoder for surrogate construction and inverse modeling (Q2237777) (← links)
• Latent map Gaussian processes for mixed variable metamodeling (Q2246360) (← links)
• ReF-nets: physics-informed neural network for Reynolds equation of gas bearing (Q2670343) (← links)
• A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials (Q2670380) (← links)
• A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335) (← links)
• Integrated finite element neural network (I-FENN) for non-local continuum damage mechanics (Q2678488) (← links)
• Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials (Q2679297) (← links)
• CPINNs: a coupled physics-informed neural networks for the closed-loop geothermal system (Q2682678) (← links)
• Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks (Q2683056) (← links)
• Isogeometric neural networks: a new deep learning approach for solving parameterized partial differential equations (Q2683423) (← links)
• Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
• Physics-integrated neural differentiable (PiNDiff) model for composites manufacturing (Q2686904) (← links)
• MFLP-PINN: a physics-informed neural network for multiaxial fatigue life prediction (Q2691055) (← links)
• A deep learning model to predict the failure response of steel pipes under pitting corrosion (Q2692887) (← links)
• A peridynamic-informed neural network for continuum elastic displacement characterization (Q2693390) (← links)
• Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate (Q2693414) (← links)
• Deep energy method in topology optimization applications (Q2694685) (← links)