Pages that link to "Item:Q2136745"

The following pages link to On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling (Q2136745):

Displaying 31 items.

• A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks (Q2020834) (← links)
• A generic physics-informed neural network-based constitutive model for soft biological tissues (Q2021025) (← links)
• Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids (Q2060111) (← links)
• Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks (Q2060125) (← links)
• Finite electro-elasticity with physics-augmented neural networks (Q2083132) (← 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)
• Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials (Q2679297) (← 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)
• Model-driven identification framework for optimal constitutive modeling from kinematics and rheological arrangement (Q6096450) (← links)
• Discovering the mechanics of artificial and real meat (Q6096485) (← links)
• A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content (Q6096512) (← links)
• \(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining (Q6101611) (← links)
• Incompressible rubber thermoelasticity: a neural network approach (Q6101617) (← links)
• On automated model discovery and a universal material subroutine for hyperelastic materials (Q6118599) (← links)
• Incremental neural controlled differential equations for modeling of path-dependent material behavior (Q6125468) (← links)
• Deep convolutional Ritz method: parametric PDE surrogates without labeled data (Q6132294) (← links)
• Efficient multiscale modeling of heterogeneous materials using deep neural networks (Q6159331) (← links)
• Bayesian synergistic metamodeling (BSM) for physical information infused data-driven metamodeling (Q6185242) (← links)
• Physics-constrained data-driven variational method for discrepancy modeling (Q6187631) (← links)
• A mechanics-informed deep learning framework for data-driven nonlinear viscoelasticity (Q6194224) (← links)
• I-FENN with temporal convolutional networks: expediting the load-history analysis of non-local gradient damage propagation (Q6497179) (← links)
• Micromechanics-based deep-learning for composites: challenges and future perspectives (Q6540411) (← links)
• Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics (Q6550128) (← links)
• Learning homogenization for elliptic operators (Q6583661) (← links)
• Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials (Q6589318) (← links)
• On sparse regression, \(L_p\)-regularization, and automated model discovery (Q6592362) (← links)
• An Eulerian constitutive model for rate-dependent inelasticity enhanced by neural networks (Q6595904) (← links)
• Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response (Q6641865) (← links)