A physics-constrained data-driven approach based on locally convex reconstruction for noisy database
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Publication:2309342
DOI10.1016/j.cma.2019.112791zbMath1436.62725arXiv1907.12651OpenAlexW3010694735MaRDI QIDQ2309342
Publication date: 31 March 2020
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1907.12651
noisy datamanifold learningdata-driven computinglocal convexity data-driven (LCDD)locally convex reconstructionreproducing kernel (RK) approximation
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Cites Work
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- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- A decomposed subspace reduction for fracture mechanics based on the meshfree integrated singular basis function method
- Imposing essential boundary conditions in mesh-free methods
- Data-driven problems in elasticity
- Machine learning strategies for systems with invariance properties
- Reproducing kernel particle methods for large deformation analysis of nonlinear structures
- Artificial neural network as an incremental nonlinear constitutive model for a finite element code.
- New boundary condition treatments in meshfree computation of contact problems
- Simple heuristic for data-driven computational elasticity with material data involving noise and outliers: a local robust regression approach
- Hidden physics models: machine learning of nonlinear partial differential equations
- Dynamic data-driven reduced-order models
- Neural network modeling for near wall turbulent flow.
- A survey of parametrized variational principles and applications to computational mechanics
- An unsupervised data completion method for physically-based data-driven models
- Model-free data-driven inelasticity
- Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials
- A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality
- Data driven computing with noisy material data sets
- Computational mechanics enhanced by deep learning
- A new reliability-based data-driven approach for noisy experimental data with physical constraints
- Data-based derivation of material response
- A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning
- A nonlinear manifold-based reduced order model for multiscale analysis of heterogeneous hyperelastic materials
- Data-driven computational mechanics
- Über ein Variationsprinzip zur Lösung von Dirichlet-Problemen bei Verwendung von Teilräumen, die keinen Randbedingungen unterworfen sind. (On a variational principle for solving Dirichlet problems less boundary conditions using subspaces)
- Robust identification of elastic properties using the modified constitutive relation error
- Computational homogenization of nonlinear elastic materials using neural networks
- 10.1162/153244304322972667
- Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
- Element‐free Galerkin methods
- Out-of-Sample Generalizations for Supervised Manifold Learning for Classification
- Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
- Inverse problems in elasticity
- Non‐linear dynamics of three‐dimensional rods: Exact energy and momentum conserving algorithms
- Reproducing kernel particle methods
- Nonlinear Dimensionality Reduction
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Data Assimilation
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