Works matching IS 01787675 AND DT 2019 AND VI 64 AND IP 2
Results: 18
Correction to: A computational mechanics special issue on: data-driven modeling and simulation—theory, methods, and applications.
- Published in:
- 2019
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- Publication type:
- Correction Notice
A computational mechanics special issue on: data-driven modeling and simulation—theory, methods, and applications.
- Published in:
- 2019
- By:
- Publication type:
- Editorial
Prediction of aerodynamic flow fields using convolutional neural networks.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 525, doi. 10.1007/s00466-019-01740-0
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- Article
Solving Bayesian inverse problems from the perspective of deep generative networks.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 395, doi. 10.1007/s00466-019-01739-7
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- Article
Model-free data-driven methods in mechanics: material data identification and solvers.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 381, doi. 10.1007/s00466-019-01731-1
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- Article
Integrated Lagrangian and Eulerian 3D microstructure-explicit simulations for predicting macroscopic probabilistic SDT thresholds of energetic materials.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 547, doi. 10.1007/s00466-019-01729-9
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- Publication type:
- Article
Derivation of heterogeneous material laws via data-driven principal component expansions.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 365, doi. 10.1007/s00466-019-01728-w
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- Article
Non-parametric material state field extraction from full field measurements.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 501, doi. 10.1007/s00466-019-01725-z
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- Publication type:
- Article
A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 467, doi. 10.1007/s00466-019-01723-1
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- Publication type:
- Article
Fast calculation of interaction tensors in clustering-based homogenization.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 351, doi. 10.1007/s00466-019-01719-x
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- Publication type:
- Article
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 417, doi. 10.1007/s00466-019-01718-y
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- Article
Clustering discretization methods for generation of material performance databases in machine learning and design optimization.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 281, doi. 10.1007/s00466-019-01716-0
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- Article
Parametric Gaussian process regression for big data.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 409, doi. 10.1007/s00466-019-01711-5
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- Publication type:
- Article
Principle of cluster minimum complementary energy of FEM-cluster-based reduced order method: fast updating the interaction matrix and predicting effective nonlinear properties of heterogeneous material.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 323, doi. 10.1007/s00466-019-01710-6
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- Publication type:
- Article
Application of deep learning neural network to identify collision load conditions based on permanent plastic deformation of shell structures.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 435, doi. 10.1007/s00466-019-01706-2
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- Article
Learning slosh dynamics by means of data.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 511, doi. 10.1007/s00466-019-01705-3
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- Publication type:
- Article
Transfer learning of deep material network for seamless structure–property predictions.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 451, doi. 10.1007/s00466-019-01704-4
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- Publication type:
- Article
A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites.
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- Computational Mechanics, 2019, v. 64, n. 2, p. 307, doi. 10.1007/s00466-018-1643-0
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- Publication type:
- Article