Pages that link to "Item:Q2020810"
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The following pages link to Model-free data-driven computational mechanics enhanced by tensor voting (Q2020810):
Displaying 28 items.
- A data-driven smoothed particle hydrodynamics method for fluids (Q1980183) (← links)
- Finite element solver for data-driven finite strain elasticity (Q2021912) (← links)
- Phase distribution and properties identification of heterogeneous materials: a data-driven approach (Q2072688) (← links)
- Data-driven finite element computation of open-cell foam structures (Q2083120) (← links)
- An investigation on the coupling of data-driven computing and model-driven computing (Q2138815) (← links)
- Model-free data-driven simulation of inelastic materials using structured data sets, tangent space information and transition rules (Q2171494) (← links)
- Data-driven reduced homogenization for transient diffusion problems with emergent history effects (Q2236925) (← links)
- A kd-tree-accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data (Q2237269) (← links)
- Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
- Data driven computing with noisy material data sets (Q2310080) (← links)
- Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: application to planar soft tissues (Q2678528) (← links)
- A new family of constitutive artificial neural networks towards automated model discovery (Q2679491) (← links)
- Distance-preserving manifold denoising for data-driven mechanics (Q2683440) (← links)
- Model-free data-driven identification algorithm enhanced by local manifold learning (Q2692900) (← links)
- Dimensionality estimation, manifold learning and function approximation using tensor voting (Q2896033) (← links)
- Material Modeling via Thermodynamics-Based Artificial Neural Networks (Q5021908) (← links)
- Model-free data-driven inelasticity in Haigh-Westergaard space -- a study how to obtain data points from measurements (Q6084497) (← links)
- Discovering the mechanics of artificial and real meat (Q6096485) (← links)
- Adaptive goal-oriented data sampling in data-driven computational mechanics (Q6101886) (← links)
- On automated model discovery and a universal material subroutine for hyperelastic materials (Q6118599) (← links)
- A data‐driven framework for evolutionary problems in solid mechanics (Q6121211) (← links)
- Deep learning framework for multiscale finite element analysis based on data-driven mechanics and data augmentation (Q6171158) (← links)
- A data-driven approach for plasticity using history surrogates: theory and application in the context of truss structures (Q6171168) (← links)
- Theory and implementation of inelastic constitutive artificial neural networks (Q6566033) (← links)
- Data-driven micromorphic mechanics for materials with strain localization (Q6588342) (← links)
- Data-driven methods for computational mechanics: a fair comparison between neural networks based and model-free approaches (Q6609807) (← links)
- Finite element approximation of data-driven problems in conductivity (Q6622728) (← links)
- Direct data-driven algorithms for multiscale mechanics (Q6663342) (← links)
