Pages that link to "Item:Q2309342"
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The following pages link to A physics-constrained data-driven approach based on locally convex reconstruction for noisy database (Q2309342):
Displaying 33 items.
- An unsupervised data completion method for physically-based data-driven models (Q1986689) (← links)
- A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks (Q2020834) (← links)
- Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity (Q2021107) (← links)
- Finite element solver for data-driven finite strain elasticity (Q2021912) (← links)
- A kernel method for learning constitutive relation in data-driven computational elasticity (Q2024599) (← links)
- Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks (Q2033658) (← links)
- PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
- Data-driven multiscale method for composite plates (Q2086060) (← links)
- An investigation on the coupling of data-driven computing and model-driven computing (Q2138815) (← links)
- Accelerating the distance-minimizing method for data-driven elasticity with adaptive hyperparameters (Q2171513) (← links)
- A kd-tree-accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data (Q2237269) (← links)
- Cell division in deep material networks applied to multiscale strain localization modeling (Q2237423) (← links)
- Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
- A new reliability-based data-driven approach for noisy experimental data with physical constraints (Q2310160) (← links)
- Geometric learning for computational mechanics. II: Graph embedding for interpretable multiscale plasticity (Q2678490) (← links)
- Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: application to planar soft tissues (Q2678528) (← links)
- Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials (Q2679297) (← links)
- Distance-preserving manifold denoising for data-driven mechanics (Q2683440) (← links)
- Model-free data-driven identification algorithm enhanced by local manifold learning (Q2692900) (← links)
- A neural network‐enhanced reproducing kernel particle method for modeling strain localization (Q6070083) (← links)
- Geometric learning for computational mechanics. III: Physics-constrained response surface of geometrically nonlinear shells (Q6096461) (← links)
- gLaSDI: parametric physics-informed greedy latent space dynamics identification (Q6107110) (← links)
- Computation‐with‐confidence for static elasticity: Data‐driven approach with order statistics (Q6121553) (← links)
- Frankenstein's data-driven computing approach to model-free mechanics (Q6159315) (← links)
- A data-driven approach for plasticity using history surrogates: theory and application in the context of truss structures (Q6171168) (← links)
- A neural network-based enrichment of reproducing kernel approximation for modeling brittle fracture (Q6185157) (← links)
- Physics-constrained data-driven variational method for discrepancy modeling (Q6187631) (← links)
- Neural-integrated meshfree (NIM) method: a differentiable programming-based hybrid solver for computational mechanics (Q6557785) (← links)
- A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems (Q6558963) (← links)
- N-adaptive Ritz method: a neural network enriched partition of unity for boundary value problems (Q6566038) (← links)
- A thermodynamics-informed neural network for elastoplastic constitutive modeling of granular materials (Q6595912) (← links)
- Data-driven methods for computational mechanics: a fair comparison between neural networks based and model-free approaches (Q6609807) (← links)
- Data-driven confidence bound for structural response using segmented least squares: a mixed-integer programming approach (Q6614962) (← links)
