Pages that link to "Item:Q2237774"
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The following pages link to Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774):
Displaying 36 items.
- Learning constitutive relations from indirect observations using deep neural networks (Q781968) (← links)
- A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks (Q2020834) (← links)
- Exploring the 3D architectures of deep material network in data-driven multiscale mechanics (Q2064793) (← links)
- Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response (Q2096901) (← links)
- Inside the black box: a physical basis for the effectiveness of deep generative models of amorphous materials (Q2133569) (← links)
- On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling (Q2136745) (← links)
- Graph neural networks for simulating crack coalescence and propagation in brittle materials (Q2142205) (← links)
- Microstructure-guided deep material network for rapid nonlinear material modeling and uncertainty quantification (Q2160409) (← links)
- Data-driven tissue mechanics with polyconvex neural ordinary differential equations (Q2160446) (← links)
- Physics-informed multi-LSTM networks for metamodeling of nonlinear structures (Q2236167) (← links)
- Theory-guided auto-encoder for surrogate construction and inverse modeling (Q2237777) (← links)
- Bayesian neural networks for uncertainty quantification in data-driven materials modeling (Q2246265) (← links)
- FEA-Net: a physics-guided data-driven model for efficient mechanical response prediction (Q2309378) (← links)
- Constrained neural network training and its application to hyperelastic material modeling (Q2667314) (← links)
- Geometric learning for computational mechanics. II: Graph embedding for interpretable multiscale plasticity (Q2678490) (← links)
- Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: application to planar soft tissues (Q2678528) (← links)
- Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials (Q2679297) (← links)
- Parametric reduced-order modeling enhancement for a geometrically imperfect component via hyper-reduction (Q2679452) (← links)
- Distance-preserving manifold denoising for data-driven mechanics (Q2683440) (← links)
- Model-free data-driven identification algorithm enhanced by local manifold learning (Q2692900) (← links)
- Molecular dynamics inferred transfer learning models for finite‐strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints (Q6070057) (← links)
- A neural network‐enhanced reproducing kernel particle method for modeling strain localization (Q6070083) (← links)
- Solving nonconvex energy minimization problems in martensitic phase transitions with a mesh-free deep learning approach (Q6084532) (← links)
- Recurrent and convolutional neural networks in structural dynamics: a modified attention steered encoder-decoder architecture versus LSTM versus GRU versus TCN topologies to predict the response of shock wave-loaded plates (Q6084770) (← links)
- Intelligent stiffness computation for plate and beam structures by neural network enhanced finite element analysis (Q6090780) (← links)
- Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics (Q6097653) (← links)
- gLaSDI: parametric physics-informed greedy latent space dynamics identification (Q6107110) (← links)
- Computation‐with‐confidence for static elasticity: Data‐driven approach with order statistics (Q6121553) (← links)
- Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response (Q6125507) (← links)
- A neural network-based enrichment of reproducing kernel approximation for modeling brittle fracture (Q6185157) (← links)
- Neural-integrated meshfree (NIM) method: a differentiable programming-based hybrid solver for computational mechanics (Q6557785) (← links)
- A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites (Q6557800) (← links)
- A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems (Q6558963) (← links)
- N-adaptive Ritz method: a neural network enriched partition of unity for boundary value problems (Q6566038) (← links)
- Data-driven micromorphic mechanics for materials with strain localization (Q6588342) (← links)
- Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials (Q6589318) (← links)
