Pages that link to "Item:Q781968"
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The following pages link to Learning constitutive relations from indirect observations using deep neural networks (Q781968):
Displaying 50 items.
- CPINet: parameter identification of path-dependent constitutive model with automatic denoising based on CNN-LSTM (Q1982319) (← links)
- Meta-modeling game for deriving theory-consistent, microstructure-based traction-separation laws via deep reinforcement learning (Q1986877) (← links)
- A general deep learning framework for history-dependent response prediction based on UA-Seq2Seq model (Q2020945) (← links)
- A generic physics-informed neural network-based constitutive model for soft biological tissues (Q2021025) (← links)
- Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity (Q2021107) (← links)
- Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening (Q2021962) (← links)
- Data-driven simulation for general-purpose multibody dynamics using deep neural networks (Q2034108) (← links)
- Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids (Q2060111) (← links)
- Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning (Q2120033) (← links)
- Image inversion and uncertainty quantification for constitutive laws of pattern formation (Q2131064) (← links)
- Hybrid FEM-NN models: combining artificial neural networks with the finite element method (Q2133536) (← links)
- Physics constrained learning for data-driven inverse modeling from sparse observations (Q2135255) (← links)
- Learning generative neural networks with physics knowledge (Q2146912) (← links)
- Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN) (Q2160403) (← links)
- Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties (Q2160432) (← links)
- Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling (Q2160481) (← links)
- Unsupervised discovery of interpretable hyperelastic constitutive laws (Q2237006) (← links)
- Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method (Q2237428) (← links)
- Learning nonlocal constitutive models with neural networks (Q2237430) (← links)
- Solving inverse problems in stochastic models using deep neural networks and adversarial training (Q2237477) (← links)
- Recurrent neural networks (RNNs) learn the constitutive law of viscoelasticity (Q2241874) (← links)
- Bayesian neural networks for uncertainty quantification in data-driven materials modeling (Q2246265) (← links)
- Learning viscoelasticity models from indirect data using deep neural networks (Q2246355) (← links)
- Constrained neural network training and its application to hyperelastic material modeling (Q2667314) (← links)
- Finite element coupled positive definite deep neural networks mechanics system for constitutive modeling of composites (Q2670357) (← links)
- Iterated Kalman methodology for inverse problems (Q2671376) (← links)
- A deep learning energy method for hyperelasticity and viscoelasticity (Q2671703) (← links)
- Data-driven elasto-(visco)-plasticity involving hidden state variables (Q2679308) (← links)
- Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
- Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
- Automated discovery of generalized standard material models with EUCLID (Q2683452) (← links)
- Physics-integrated neural differentiable (PiNDiff) model for composites manufacturing (Q2686904) (← links)
- Implicit constitutive modelling for viscoplasticity using neural networks (Q4253789) (← links)
- Material Modeling via Thermodynamics-Based Artificial Neural Networks (Q5021908) (← links)
- Efficient derivative-free Bayesian inference for large-scale inverse problems (Q5044981) (← links)
- Probabilistic partition of unity networks for high‐dimensional regression problems (Q6062830) (← links)
- Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution (Q6068233) (← links)
- A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling (Q6069980) (← links)
- Molecular dynamics inferred transfer learning models for finite‐strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints (Q6070057) (← links)
- Bayesian calibration for large‐scale fluid structure interaction problems under embedded/immersed boundary framework (Q6092206) (← links)
- Addressing discontinuous root-finding for subsequent differentiability in machine learning, inverse problems, and control (Q6119249) (← links)
- DPK: Deep Neural Network Approximation of the First Piola-Kirchhoff Stress (Q6141664) (← links)
- Improved Analysis of PINNs: Alleviate the CoD for Compositional Solutions (Q6151354) (← links)
- An equivariant neural operator for developing nonlocal tensorial constitutive models (Q6162914) (← links)
- Spectral operator learning for parametric PDEs without data reliance (Q6194143) (← links)
- A mechanics-informed deep learning framework for data-driven nonlinear viscoelasticity (Q6194224) (← links)
- An indirect training approach for implicit constitutive modelling using recurrent neural networks and the virtual fields method (Q6497200) (← links)
- NN-mCRE: a modified constitutive relation error framework for unsupervised learning of nonlinear state laws with physics-augmented neural networks (Q6499924) (← links)
- A computationally tractable framework for nonlinear dynamic multiscale modeling of membrane woven fabrics (Q6553966) (← links)
- Physical informed neural network for thermo-hydral analysis of fire-loaded concrete (Q6566857) (← links)
