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Computational science

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Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science that uses advanced computing capabilities to understand and solve complex physical problems. This includes

In practical use, it is typically the application of computer simulation and other forms of computation from numerical analysis and theoretical computer science to solve problems in various scientific disciplines. The field is different from theory and laboratory experiments, which are the traditional forms of science and engineering. The scientific computing approach is to gain understanding through the analysis of mathematical models implemented on computers. Scientists and engineers develop computer programs and application software that model systems being studied and run these programs with various sets of input parameters. The essence of computational science is the application of numerical algorithms[1] and computational mathematics. In some cases, these models require massive amounts of calculations (usually floating-point) and are often executed on supercomputers or distributed computing platforms.[verification needed]

The computational scientist

Ways to study a system

The term computational scientist is used to describe someone skilled in scientific computing. Such a person is usually a scientist, an engineer, or an applied mathematician who applies high-performance computing in different ways to advance the state-of-the-art in their respective applied disciplines in physics, chemistry, or engineering.

Computational science is now commonly considered a third mode of science [citation needed], complementing and adding to experimentation/observation and theory (see image).[2] Here, one defines a system as a potential source of data,[3] an experiment as a process of extracting data from a system by exerting it through its inputs[4] and a model (M) for a system (S) and an experiment (E) as anything to which E can be applied in order to answer questions about S.[5] A computational scientist should be capable of:

  • recognizing complex problems
  • adequately conceptualizing the system containing these problems
  • designing a framework of algorithms suitable for studying this system: the simulation
  • choosing a suitable computing infrastructure (parallel computing/grid computing/supercomputers)
  • hereby, maximizing the computational power of the simulation
  • assessing to what level the output of the simulation resembles the systems: the model is validated
  • adjusting the conceptualization of the system accordingly
  • repeat the cycle until a suitable level of validation is obtained: the computational scientist trusts that the simulation generates adequately realistic results for the system under the studied conditions

Substantial effort in computational sciences has been devoted to developing algorithms, efficient implementation in programming languages, and validating computational results. A collection of problems and solutions in computational science can be found in Steeb, Hardy, Hardy, and Stoop (2004).[6]

Philosophers of science addressed the question to what degree computational science qualifies as science, among them Humphreys[7] and Gelfert.[8] They address the general question of epistemology: how does gain insight from such computational science approaches? Tolk[9] uses these insights to show the epistemological constraints of computer-based simulation research. As computational science uses mathematical models representing the underlying theory in executable form, in essence, they apply modeling (theory building) and simulation (implementation and execution). While simulation and computational science are our most sophisticated way to express our knowledge and understanding, they also come with all constraints and limits already known for computational solutions.[citation needed]

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.[10] 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".[11]

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]

Computational finance


In financial markets, 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 and computational models. 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 algorithms. This has become even more urgent and complex recently, as the credit crisis[which?] has clearly[according to whom?] demonstrated the role of cascading effects[which?] 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]

Computational biology


Exciting new developments in biotechnology are now revolutionizing biology and biomedical research. Examples of these techniques are high-throughput sequencing, high-throughput quantitative PCR, intra-cellular imaging, in-situ hybridization of gene expression, three-dimensional imaging techniques like Light Sheet Fluorescence Microscopy, and 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 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 molecules interacting with the mineral deposition process, 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 and spatial scales (ranging from nanometer and nanoseconds to meters and years) are connected into a multi-scale system. One of the few available options[which?] to understand such systems is by developing a multi-scale model of the system.[citation needed]

Complex systems theory


Using information theory, non-equilibrium dynamics, and explicit simulations, computational systems theory tries to uncover the true nature of complex adaptive systems.[citation needed]

Computational science and engineering


Computational science and engineering (CSE) is a relatively new[quantify] 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).[12] 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]

Methods and algorithms


Algorithms and mathematical methods used in computational science are varied. Commonly applied methods include:

Historically and today, Fortran remains popular for most applications of scientific computing.[32][33] Other programming languages and computer algebra systems commonly used for the more mathematical aspects of scientific computing applications include GNU Octave, Haskell,[32] Julia,[32] Maple,[33] Mathematica,[34][35][36][37][38] MATLAB,[39][40][41] Python (with third-party SciPy library[42][43][44]), Perl (with third-party PDL library),[citation needed] R,[45] Scilab,[46][47] and TK Solver. The more computationally intensive aspects of scientific computing will often use some variation of C or Fortran and optimized algebra libraries such as BLAS or LAPACK. In addition, parallel computing is heavily used in scientific computing to find solutions of large problems in a reasonable amount of time. In this framework, the problem is either divided over many cores on a single CPU node (such as with OpenMP), divided over many CPU nodes networked together (such as with MPI), or is run on one or more GPUs (typically using either CUDA or OpenCL).

Computational science application programs often model real-world changing conditions, such as weather, airflow around a plane, automobile body distortions in a crash, the motion of stars in a galaxy, an explosive device, etc. Such programs might create a 'logical mesh' in computer memory where each item corresponds to an area in space and contains information about that space relevant to the model. For example, in weather models, each item might be a square kilometer; with land elevation, current wind direction, humidity, temperature, pressure, etc. The program would calculate the likely next state based on the current state, in simulated time steps, solving differential equations that describe how the system operates, and then repeat the process to calculate the next state.[citation needed]

Conferences and journals


In 2001, the International Conference on Computational Science (ICCS) was first organized. Since then, it has been organized yearly. ICCS is an A-rank conference in the CORE ranking.[48]

The Journal of Computational Science published its first issue in May 2010.[49][50][51] The Journal of Open Research Software was launched in 2012.[52] The ReScience C initiative, which is dedicated to replicating computational results, was started on GitHub in 2015.[53]



At some institutions, a specialization in scientific computation can be earned as a "minor" within another program (which may be at varying levels). However, there are increasingly many bachelor's, master's, and doctoral programs in computational science. The joint degree program master program computational science at the University of Amsterdam and the Vrije Universiteit in computational science was first offered in 2004. In this program, students:

  • learn to build computational models from real-life observations;
  • develop skills in turning these models into computational structures and in performing large-scale simulations;
  • learn theories[which?] that will give a firm basis for the analysis of complex systems;
  • learn to analyze the results of simulations in a virtual laboratory using advanced numerical algorithms.[relevant?]

ETH Zurich offers a bachelor's and master's degree in Computational Science and Engineering. The degree equips students with the ability to understand scientific problem and apply numerical methods to solve such problems. The directions of specializations include Physics, Chemistry, Biology and other Scientific and Engineering disciplines.

George Mason University has offered a multidisciplinary doctorate Ph.D. program in Computational Sciences and Informatics starting from 1992.[54]

The School of Computational and Integrative Sciences, Jawaharlal Nehru University (erstwhile School of Information Technology[55][verification needed]) also offers[verification needed] a vibrant master's science program for computational science with two specialties: Computational Biology and Complex Systems.[56]



See also



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  4. ^ Cellier, François (1990). Continuous System Modelling.
  5. ^ Minski, Marvin (1965). Models, Minds, Machines.
  6. ^ Steeb W.-H., Hardy Y., Hardy A. and Stoop R., 2004. Problems and Solutions in Scientific Computing with C++ and Java Simulations, World Scientific Publishing. ISBN 981-256-112-9
  7. ^ Humphreys, Paul. Extending ourselves: Computational science, empiricism, and scientific method. Oxford University Press, 2004.
  8. ^ Gelfert, Axel. 2016. How to do science with models: A philosophical primer. Cham: Springer.
  9. ^ Tolk, Andreas. "Learning Something Right from Models That Are Wrong: Epistemology of Simulation." In Concepts and Methodologies for Modeling and Simulation, edited by L. Yilmaz, pp. 87-106, Cham: Springer International Publishing, 2015.
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  11. ^ Szabó B, Actis R and Rusk D. Validation of notch sensitivity factors. Journal of Verification, Validation and Uncertainty Quantification. 4 011004, 2019
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  53. ^ Rougier, Nicolas P.; Hinsen, Konrad; Alexandre, Frédéric; Arildsen, Thomas; Barba, Lorena A.; Benureau, Fabien C.Y.; Brown, C. Titus; Buyl, Pierre de; Caglayan, Ozan; Davison, Andrew P.; Delsuc, Marc-André; Detorakis, Georgios; Diem, Alexandra K.; Drix, Damien; Enel, Pierre; Girard, Benoît; Guest, Olivia; Hall, Matt G.; Henriques, Rafael N.; Hinaut, Xavier; Jaron, Kamil S.; Khamassi, Mehdi; Klein, Almar; Manninen, Tiina; Marchesi, Pietro; McGlinn, Daniel; Metzner, Christoph; Petchey, Owen; Plesser, Hans Ekkehard; Poisot, Timothée; Ram, Karthik; Ram, Yoav; Roesch, Etienne; Rossant, Cyrille; Rostami, Vahid; Shifman, Aaron; Stachelek, Joseph; Stimberg, Marcel; Stollmeier, Frank; Vaggi, Federico; Viejo, Guillaume; Vitay, Julien; Vostinar, Anya E.; Yurchak, Roman; Zito, Tiziano (December 2017). "Sustainable computational science: the ReScience initiative". PeerJ Comput Sci. 3. e142. arXiv:1707.04393. Bibcode:2017arXiv170704393R. doi:10.7717/peerj-cs.142. PMC 8530091. PMID 34722870. S2CID 7392801.
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