Pages that link to "Item:Q2667314"
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The following pages link to Constrained neural network training and its application to hyperelastic material modeling (Q2667314):
Displaying 20 items.
- Truncated-Newton training algorithm for neurocomputational viscoplastic model. (Q1418239) (← links)
- A generic physics-informed neural network-based constitutive model for soft biological tissues (Q2021025) (← links)
- Finite electro-elasticity with physics-augmented neural networks (Q2083132) (← links)
- Data-driven tissue mechanics with polyconvex neural ordinary differential equations (Q2160446) (← links)
- Predicting the mechanical properties of biopolymer gels using neural networks trained on discrete fiber network data (Q2246386) (← links)
- Distance-preserving manifold denoising for data-driven mechanics (Q2683440) (← links)
- Application of a Hopfield type neural network to the analysis of elastic problems with unilateral constraints (Q2710661) (← links)
- Numerical implementation of a neural network based material model in finite element analysis (Q4462927) (← links)
- Advanced discretization techniques for hyperelastic physics-augmented neural networks (Q6062433) (← links)
- A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling (Q6069980) (← links)
- Physically enhanced training for modeling rate-independent plasticity with feedforward neural networks (Q6084775) (← links)
- Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations (Q6097591) (← 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)
- Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria (Q6121688) (← links)
- Nonlinear electro-elastic finite element analysis with neural network constitutive models (Q6497139) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks (Q6604129) (← links)
- Data-driven methods for computational mechanics: a fair comparison between neural networks based and model-free approaches (Q6609807) (← links)
- Non-intrusive parametric hyper-reduction for nonlinear structural finite element formulations (Q6669042) (← links)
