Pages that link to "Item:Q2060125"
From MaRDI portal
The following pages link to Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks (Q2060125):
Displaying 19 items.
- Recurrent neural networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step (Q2072735) (← links)
- The mixed deep energy method for resolving concentration features in finite strain hyperelasticity (Q2134762) (← links)
- On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling (Q2136745) (← links)
- Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties (Q2160432) (← links)
- Micromechanics-based surrogate models for the response of composites: a critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks (Q2190108) (← links)
- Geometric learning for computational mechanics. II: Graph embedding for interpretable multiscale plasticity (Q2678490) (← links)
- Enhancing phenomenological yield functions with data: challenges and opportunities (Q2692822) (← links)
- Modular machine learning-based elastoplasticity: generalization in the context of limited data (Q2693407) (← links)
- Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data (Q6049615) (← links)
- \(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining (Q6101611) (← links)
- GPLaSDI: Gaussian process-based interpretable latent space dynamics identification through deep autoencoder (Q6118601) (← links)
- Gradient enhanced Gaussian process regression for constitutive modelling in finite strain hyperelasticity (Q6120133) (← links)
- Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria (Q6121688) (← links)
- Deep convolutional Ritz method: parametric PDE surrogates without labeled data (Q6132294) (← links)
- An asynchronous parallel high-throughput model calibration framework for crystal plasticity finite element constitutive models (Q6164276) (← links)
- Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics (Q6550128) (← links)
- Nonlinear response modelling of material systems using constrained Gaussian processes (Q6592364) (← links)
- Learning nonlinear constitutive models in finite strain electromechanics with Gaussian process predictors (Q6630898) (← links)
- A reduced order variational spectral method for efficient construction of eigenstrain-based reduced order homogenization models (Q6648529) (← links)
