Thermodynamically consistent data-driven computational mechanics
Article Properties
-
LanguageEnglish
-
DOI (url)
-
Publication Date2018/05/17
-
Indian UGC (journal)
-
Refrences36
-
Citations57
-
David González
-
Francisco Chinesta
-
Elías Cueto ORCID (unauthenticated)
Cite
González, David, et al. “Thermodynamically Consistent Data-Driven Computational Mechanics”. Continuum Mechanics and Thermodynamics, vol. 31, no. 1, 2018, pp. 239-53, https://doi.org/10.1007/s00161-018-0677-z.
González, D., Chinesta, F., & Cueto, E. (2018). Thermodynamically consistent data-driven computational mechanics. Continuum Mechanics and Thermodynamics, 31(1), 239-253. https://doi.org/10.1007/s00161-018-0677-z
González D, Chinesta F, Cueto E. Thermodynamically consistent data-driven computational mechanics. Continuum Mechanics and Thermodynamics. 2018;31(1):239-53.
Journal Categories
Science
Physics
Heat
Thermodynamics
Technology
Engineering (General)
Civil engineering (General)
Technology
Engineering (General)
Civil engineering (General)
Mechanics of engineering
Applied mechanics
Technology
Mechanical engineering and machinery
You May Also Like
- Truss Modular Beams with Deformation Energy Depending on Higher Displacement Gradients
- At the origins and in the vanguard of peridynamics, non-local and higher-gradient continuum mechanics: An underestimated and still topical contribution of Gabrio Piola
- Generalization of Drucker's Yield Criterion to Orthotropy
- On frame indifferent formulation of the Maxwell–Cattaneo model of finite-speed heat conduction
- Classification of cracking mode in concrete by acoustic emission parameters
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Data-driven non-linear elasticity: constitutive manifold construction and problem discretization | Computational Mechanics |
| 91 | 2017 |
| Numerical investigation of a particle system compared with first and second gradient continua: Deformation and fracture phenomena* | Mathematics and Mechanics of Solids |
| 27 | 2017 |
Discovering governing equations from data by sparse identification of nonlinear dynamical systems | Proceedings of the National Academy of Sciences |
| 1,785 | 2016 |
| 10.1615/IntJMultCompEng.v9.i1.20 | International Journal for Multiscale Computational Engineering |
| 2011 | |
| Algorithms for coupled problems that preserve symmetries and the laws of thermodynamics | Computer Methods in Applied Mechanics and Engineering |
| 50 | 2010 |
Refrences Analysis
The category
Technology: Engineering (General). Civil engineering (General) 18
is the most frequently represented among the references in this article. It primarily includes studies from Computer Methods in Applied Mechanics and Engineering The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Physics-Based Self-Learning Spiking Neural Network enhanced time-integration scheme for computing viscoplastic structural finite element response | Computer Methods in Applied Mechanics and Engineering |
| 2024 | |
A hybrid twin based on machine learning enhanced reduced order model for real-time simulation of magnetic bearings | Advanced Modeling and Simulation in Engineering Sciences |
| 2024 | |
Physically constrained deep recurrent neural network for stiffness computation of plate structures | PAMM | 3 | 2023 | |
| A novel outlier-insensitive local support vector machine for robust data-driven forecasting in engineering | Engineering with Computers |
| 1 | 2023 |
FE$${}^\textrm{ANN}$$: an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining | Computational Mechanics |
| 21 | 2023 |
Citations Analysis
The category
Technology: Engineering (General). Civil engineering (General): Mechanics of engineering. Applied mechanics 28
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Virtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data and was published in 2018. The most recent citation comes from a 2024 study titled Physics-Based Self-Learning Spiking Neural Network enhanced time-integration scheme for computing viscoplastic structural finite element response. This article reached its peak citation in 2023, with 15 citations. It has been cited in 27 different journals, 33% of which are open access. Among related journals, the Computer Methods in Applied Mechanics and Engineering cited this research the most, with 12 citations. The chart below illustrates the annual citation trends for this article.