Pages that link to "Item:Q2679491"
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The following pages link to A new family of constitutive artificial neural networks towards automated model discovery (Q2679491):
Displaying 34 items.
- CANN (Q1353321) (← links)
- Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning (Q2120033) (← links)
- Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data (Q6049615) (← links)
- Advanced discretization techniques for hyperelastic physics-augmented neural networks (Q6062433) (← links)
- Discrete data-adaptive approximation of hyperelastic energy functions (Q6062465) (← links)
- Automated model discovery for skin: discovering the best model, data, and experiment (Q6094670) (← 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)
- Surrogate modeling for the homogenization of elastoplastic composites based on RBF interpolation (Q6096506) (← links)
- On automated model discovery and a universal material subroutine for hyperelastic materials (Q6118599) (← links)
- Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria (Q6121688) (← links)
- Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions (Q6125484) (← links)
- A computational framework for nanotrusses: input convex neural networks approach (Q6181423) (← links)
- Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression (Q6185193) (← links)
- Viscoelastic constitutive artificial neural networks (vCANNs) -- a framework for data-driven anisotropic nonlinear finite viscoelasticity (Q6196602) (← links)
- Nonlinear electro-elastic finite element analysis with neural network constitutive models (Q6497139) (← links)
- Peridynamic neural operators: a data-driven nonlocal constitutive model for complex material responses (Q6497150) (← links)
- Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics (Q6550128) (← links)
- A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites (Q6557800) (← links)
- Theory and implementation of inelastic constitutive artificial neural networks (Q6566033) (← links)
- Automated model discovery for human cardiac tissue: discovering the best model and parameters (Q6566049) (← links)
- A robust radial point interpolation method empowered with neural network solvers (RPIM-NNS) for nonlinear solid mechanics (Q6588318) (← 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)
- Versatile data-adaptive hyperelastic energy functions for soft materials (Q6595875) (← links)
- A thermodynamics-informed neural network for elastoplastic constitutive modeling of granular materials (Q6595912) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Data-driven methods for computational mechanics: a fair comparison between neural networks based and model-free approaches (Q6609807) (← links)
- Artificial neural network design for non linear Takagi-Sugeno systems: application to tracking of trajectory, state and fault estimation of MIABOT robot (Q6618992) (← links)
- Stretch-based hyperelastic constitutive metamodels via gradient enhanced Gaussian predictors (Q6643567) (← links)
- Bayesian neural networks for predicting uncertainty in full-field material response (Q6663292) (← links)
- Discovering uncertainty: Bayesian constitutive artificial neural networks (Q6663336) (← links)
- Non-intrusive parametric hyper-reduction for nonlinear structural finite element formulations (Q6669042) (← links)
- Machine-learning-based virtual fields method: application to anisotropic hyperelasticity (Q6669068) (← links)
