Computational model

Short description: Mathematical model of a complex system

For mathematical models of computers, see Model of computation.

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

1. ↑ 1.0 1.1 "Computational Modeling" (in en). May 2020. https://www.nibib.nih.gov/science-education/science-topics/computational-modeling.
2. ↑ "Computational Modelling in Space Physics". https://www.frontiersin.org/research-topics/39950/computational-modelling-in-space-physics.
3. ↑ "What is Computational Engineering?". https://www.ae.utexas.edu/undergraduate/computational-undergrad-program/what-is-computational-engineering.
4. ↑ 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.
5. ↑ Davey, Reginald (2021-04-14). "Computational Modeling in Developmental Biology" (in en). https://www.news-medical.net/life-sciences/Computational-Modeling-in-Developmental-Biology.aspx.
6. ↑ 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.
7. ↑ "Computational models - Latest research and news". https://www.nature.com/subjects/computational-models.
8. ↑ 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. doi:10.1016/j.cma.2025.117986.
9. ↑ 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. doi:10.1016/j.cma.2023.116049.
10. ↑ "Computational Modelling in Engineering" (in en). https://www.fer.unizg.hr/en/studies/cro/master/fer3/computing/cme.

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