Editing Computational science (section)

== Applications of computational science ==
Problem domains for computational science/scientific computing include:

=== Predictive computational science ===
Predictive computational science is a scientific discipline concerned with the formulation, calibration, numerical solution, and validation of mathematical models designed to predict specific aspects of physical events, given initial and boundary conditions, and a set of characterizing parameters and associated uncertainties.<ref>Oden, J.T., Babuška, I. and Faghihi, D., 2017. Predictive computational science: Computer predictions in the presence of uncertainty. Encyclopedia of Computational Mechanics. Second Edition, pp. 1-26.</ref> In typical cases, the predictive statement is formulated in terms of probabilities. For example, given a mechanical component and a periodic loading condition, "the probability is (say) 90% that the number of cycles at failure (Nf) will be in the interval N1<Nf<N2".<ref>Szabó B, Actis R and Rusk D.  Validation of notch sensitivity factors. Journal of Verification, Validation and Uncertainty Quantification.  4 011004, 2019</ref>

=== Urban complex systems ===
Cities are massively complex systems created by humans, made up of humans, and governed by humans. Trying to predict, understand and somehow shape the development of cities in the future requires complex thinking and computational models and simulations to help mitigate challenges and possible disasters. The focus of research in urban complex systems is, through modeling and simulation, to build a greater understanding of city dynamics and help prepare for the coming [[urbanization]].{{Citation needed|date=December 2021}}

=== Computational finance ===
{{main|Computational finance}}
In [[financial market]]s, huge volumes of interdependent assets are traded by a large number of interacting market participants in different locations and time zones. Their behavior is of unprecedented complexity and the characterization and measurement of the risk inherent to this highly diverse set of instruments is typically based on complicated [[Mathematical model|mathematical]] and [[computational model]]s. Solving these models exactly in closed form, even at a single instrument level, is typically not possible, and therefore we have to look for efficient [[numerical algorithm]]s. This has become even more urgent and complex recently, as the credit crisis{{Which|date=December 2021}} has clearly{{According to whom|date=December 2021}} demonstrated the role of cascading effects{{Which|date=December 2021}} going from single instruments through portfolios of single institutions to even the interconnected trading network. Understanding this requires a multi-scale and holistic approach where interdependent risk factors such as market, credit, and liquidity risk are modeled simultaneously and at different interconnected scales.{{Citation needed|date=December 2021}}

=== Computational biology ===
{{main|Computational biology}}
Exciting new developments in [[biotechnology]] are now revolutionizing biology and [[biomedical research]]. Examples of these techniques are [[DNA sequencing|high-throughput sequencing]], high-throughput [[Real-time polymerase chain reaction|quantitative PCR]], intra-cellular imaging, [[In situ hybridization|in-situ hybridization]] of gene expression, three-dimensional imaging techniques like [[Light sheet fluorescence microscopy|Light Sheet Fluorescence Microscopy]], and [[Optical projection tomography|Optical Projection (micro)-Computer Tomography]]. Given the massive amounts of complicated data that is generated by these techniques, their meaningful interpretation, and even their storage, form major challenges calling for new approaches. Going beyond current bioinformatics approaches, [[computational biology]] needs to develop new methods to discover meaningful patterns in these large data sets. Model-based reconstruction of [[Gene regulatory network|gene networks]] can be used to organize the gene expression data in a systematic way and to guide future data collection. A major challenge here is to understand how gene regulation is controlling fundamental biological processes like [[biomineralization]] and [[embryogenesis]]. The sub-processes like [[gene regulation]], [[Organic compound|organic molecules]] interacting with the mineral deposition process, [[Cell (biology)|cellular processes]], [[physiology]], and other processes at the tissue and environmental levels are linked. Rather than being directed by a central control mechanism, biomineralization and embryogenesis can be viewed as an emergent behavior resulting from a complex system in which several sub-processes on very different [[Temporal scales|temporal]] and [[spatial scale]]s (ranging from nanometer and nanoseconds to meters and years) are connected into a multi-scale system. One of the few available options{{Which|date=December 2021}} to understand such systems is by developing a [[Multiscale modeling|multi-scale model]] of the system.{{Citation needed|date=December 2021}}

=== Complex systems theory ===
{{main|Complex systems}}
Using [[information theory]], [[Non-equilibrium thermodynamics|non-equilibrium dynamics]], and explicit simulations, computational systems theory tries to uncover the true nature of [[complex adaptive system]]s.{{Citation needed|date=December 2021}}

=== Computational science and engineering ===
{{main|Computational engineering}}

Computational science and engineering (CSE) is a relatively new{{Quantify|date=December 2021}} discipline that deals with the development and application of computational models and simulations, often coupled with [[high-performance computing]], to solve complex physical problems arising in engineering analysis and design (computational engineering) as well as natural phenomena (computational science). CSE has become accepted amongst scientists, engineers and academics as the "third mode of discovery" (next to theory and experimentation).<ref>{{Cite web |url=http://www.cseprograms.gatech.edu/sites/default/files/CSEHandbook-Students-v11.pdf |title=Computational Science and Engineering Program: Graduate Student Handbook |website=cseprograms.gatech.edu |date=September 2009 |access-date=2017-08-26 |archive-url=https://web.archive.org/web/20141014001918/http://www.cseprograms.gatech.edu/sites/default/files/CSEHandbook-Students-v11.pdf |archive-date=2014-10-14 |url-status=dead }}</ref> In many fields, computer simulation is integral and therefore essential to business and research. Computer simulation provides the capability to enter fields that are either inaccessible to traditional experimentation or where carrying out traditional empirical inquiries is prohibitively expensive. CSE should neither be confused with pure [[computer science]], nor with [[computer engineering]], although a wide domain in the former is used in CSE (e.g., certain algorithms, data structures, parallel programming, high-performance computing), and some problems in the latter can be modeled and solved with CSE methods (as an application area).{{Citation needed|date=December 2021}}