Computational model
In this article, the topic of Computational model will be addressed from different perspectives, with the purpose of exploring its implications, applications and relevance today. Its historical context, its possible impacts in various areas and its relevance in the current panorama will be analyzed in detail. Likewise, its possible future implications will be delved into and various points of view on Computational model will be discussed. Through a journey through different approaches and opinions, the aim is to provide the reader with a comprehensive and detailed vision of this topic, with the aim of encouraging debate and reflection.
A computational model uses computers to simulate and study complex systems[1] using an algorithmic or mechanistic approach and is widely used in a diverse range of fields spanning from physics,[2] engineering,[3] chemistry[4] and biology[5] to economics, psychology, cognitive science and computer science.[1]
The system under study is often a complex nonlinear system[6] for which simple, intuitive analytical solutions are not readily available. Rather than deriving a mathematical analytical solution to the problem, experimentation with the model is done by adjusting the parameters of the system in the computer, and studying the differences in the outcome of the experiments.[7] Operation theories of the model can be derived/deduced from these computational experiments.
Examples of common computational models are weather forecasting models, earth simulator models, flight simulator models, virtual cells, molecular protein folding models, computational materials models[8][9] Computational Engineering Models (CEM),[10] and neural network models.
See also
- Computational engineering
- Computational cognition
- Reversible computing
- Agent-based model
- Artificial neural network
- Computational linguistics
- Data-driven model
- Decision field theory
- Dynamical systems model of cognition
- Membrane computing
- Ontology (information science)
- Programming language theory
- Microscale and macroscale models
References
- ^ a b "Computational Modeling". National Institute of Biomedical Imaging and Bioengineering. May 2020. Retrieved 2023-06-27.
- ^ "Computational Modelling in Space Physics". Frontiers Research Topic. Archived from the original on 2023-06-27. Retrieved 2023-06-27.
- ^ "What is Computational Engineering?". Dept of Aerospace Engineering & Engineering Mechanics - Cockrell School of Engineering, The University of Texas at Austin. Retrieved 2023-06-27.
- ^ Amarante, A.M.; Oliveira, G.S.; Ierich, J.C.M.; Cunha, R.A.; Freitas, L.C.G.; Franca, E.F.; De Lima Leite, F. (2017). "Molecular Modeling Applied to Nanobiosystems". Nanoscience and its Applications. pp. 179–220. doi:10.1016/B978-0-323-49780-0.00007-7. ISBN 978-0-323-49780-0.
- ^ Davey, Reginald (2021-04-14). "Computational Modeling in Developmental Biology". News-Medical.net. Retrieved 2023-06-27.
- ^ Grubb, Amanda L.; Moushegian, Alex; Heathcote, Daniel J.; Smith, Marilyn J. (2020). "Physics and Computational Modeling of Nonlinear Transverse Gust Encounters". AIAA Scitech 2020 Forum. doi:10.2514/6.2020-0080. ISBN 978-1-62410-595-1.
- ^ "Computational models - Latest research and news". Nature. Retrieved 2021-04-08.
- ^ Fu, Jinlong; Tan, Wei (2025). "Stochastic reconstruction of multiphase composite microstructures using statistics-encoded neural network for poro/micro-mechanical modelling". Computer Methods in Applied Mechanics and Engineering. 441 117986. doi:10.1016/j.cma.2025.117986.
- ^ Fu, Jinlong; Wang, Min; Xiao, Dunhui; Zhong, Shan; Ge, Xiangyun; Wu, Minglu (2023). "Hierarchical reconstruction of 3D well-connected porous media from 2D exemplars using statistics-informed neural network". Computer Methods in Applied Mechanics and Engineering. 410 116049. doi:10.1016/j.cma.2023.116049.
- ^ "Computational Modelling in Engineering". University of Zagreb Faculty of Electrical Engineering and Computing. Retrieved 2023-06-27.