fbpx
Wikipedia

Computational science

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 edit

 
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 edit

Problem domains for computational science/scientific computing include:

Predictive computational science edit

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 edit

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 edit

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 edit

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 edit

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 edit

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 been described[by whom?] as the "third mode of discovery" (next to theory and experimentation).[12] In many fields[which?], computer simulation is integral and therefore essential to business and research. Computer simulation provides the capability to enter fields[which?] 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 edit

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 edit

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]

Education edit

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]

Subfields edit

See also edit

References edit

  1. ^ Nonweiler T. R., 1986. Computational Mathematics: An Introduction to Numerical Approximation, John Wiley and Sons
  2. ^ Graduate Education for Computational Science and Engineering.Siam.org, Society for Industrial and Applied Mathematics (SIAM) website; accessed Feb 2013.
  3. ^ Siegler, Bernard (1976). Theory of Modeling and Simulation.
  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.
  10. ^ 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.
  11. ^ Szabó B, Actis R and Rusk D. Validation of notch sensitivity factors. Journal of Verification, Validation and Uncertainty Quantification. 4 011004, 2019
  12. ^ (PDF). cseprograms.gatech.edu. September 2009. Archived from the original (PDF) on 2014-10-14. Retrieved 2017-08-26.
  13. ^ Von Zur Gathen, J., & Gerhard, J. (2013). Modern computer algebra. Cambridge University Press.
  14. ^ Geddes, K. O., Czapor, S. R., & Labahn, G. (1992). Algorithms for computer algebra. Springer Science & Business Media.
  15. ^ Albrecht, R. (2012). Computer algebra: symbolic and algebraic computation (Vol. 4). Springer Science & Business Media.
  16. ^ Mignotte, M. (2012). Mathematics for computer algebra. Springer Science & Business Media.
  17. ^ Stoer, J., & Bulirsch, R. (2013). Introduction to numerical analysis. Springer Science & Business Media.
  18. ^ Conte, S. D., & De Boor, C. (2017). Elementary numerical analysis: an algorithmic approach. Society for Industrial and Applied Mathematics.
  19. ^ Greenspan, D. (2018). Numerical Analysis. CRC Press.
  20. ^ Linz, P. (2019). Theoretical numerical analysis. Courier Dover Publications.
  21. ^ Brenner, S., & Scott, R. (2007). The mathematical theory of finite element methods (Vol. 15). Springer Science & Business Media.
  22. ^ Oden, J. T., & Reddy, J. N. (2012). An introduction to the mathematical theory of finite elements. Courier Corporation.
  23. ^ Davis, P. J., & Rabinowitz, P. (2007). Methods of numerical integration. Courier Corporation.
  24. ^ Peter Deuflhard, Newton Methods for Nonlinear Problems. Affine Invariance and Adaptive Algorithms, Second printed edition. Series Computational Mathematics 35, Springer (2006)
  25. ^ Hammersley, J. (2013). Monte carlo methods. Springer Science & Business Media.
  26. ^ Kalos, M. H., & Whitlock, P. A. (2009). Monte carlo methods. John Wiley & Sons.
  27. ^ Demmel, J. W. (1997). Applied numerical linear algebra. SIAM.
  28. ^ Ciarlet, P. G., Miara, B., & Thomas, J. M. (1989). Introduction to numerical linear algebra and optimization. Cambridge University Press.
  29. ^ Trefethen, Lloyd; Bau III, David (1997). Numerical Linear Algebra (1st ed.). Philadelphia: SIAM.
  30. ^ Vanderbei, R. J. (2015). Linear programming. Heidelberg: Springer.
  31. ^ Gass, S. I. (2003). Linear programming: methods and applications. Courier Corporation.
  32. ^ a b c Phillips, Lee (2014-05-07). "Scientific computing's future: Can any coding language top a 1950s behemoth?". Ars Technica. Retrieved 2016-03-08.
  33. ^ a b Landau, Rubin (2014-05-07). "A First Course in Scientific Computing" (PDF). Princeton University. Retrieved 2016-03-08.
  34. ^ Mathematica 6 2011-01-13 at the Wayback Machine Scientific Computing World, May 2007
  35. ^ Maeder, R. E. (1991). Programming in mathematica. Addison-Wesley Longman Publishing Co., Inc.
  36. ^ Stephen Wolfram. (1999). The MATHEMATICA® book, version 4. Cambridge University Press.
  37. ^ Shaw, W. T., & Tigg, J. (1993). Applied Mathematica: getting started, getting it done. Addison-Wesley Longman Publishing Co., Inc.
  38. ^ Marasco, A., & Romano, A. (2001). Scientific Computing with Mathematica: Mathematical Problems for Ordinary Differential Equations; with a CD-ROM. Springer Science & Business Media.
  39. ^ Quarteroni, A., Saleri, F., & Gervasio, P. (2006). Scientific computing with MATLAB and Octave. Berlin: Springer.
  40. ^ Gander, W., & Hrebicek, J. (Eds.). (2011). Solving problems in scientific computing using Maple and Matlab®. Springer Science & Business Media.
  41. ^ Barnes, B., & Fulford, G. R. (2011). Mathematical modelling with case studies: a differential equations approach using Maple and MATLAB. Chapman and Hall/CRC.
  42. ^ Jones, E., Oliphant, T., & Peterson, P. (2001). SciPy: Open source scientific tools for Python.
  43. ^ Bressert, E. (2012). SciPy and NumPy: an overview for developers. " O'Reilly Media, Inc.".
  44. ^ Blanco-Silva, F. J. (2013). Learning SciPy for numerical and scientific computing. Packt Publishing Ltd.
  45. ^ Ihaka, R., & Gentleman, R. (1996). R: a language for data analysis and graphics. Journal of computational and graphical statistics, 5(3), 299-314.
  46. ^ Bunks, C., Chancelier, J. P., Delebecque, F., Goursat, M., Nikoukhah, R., & Steer, S. (2012). Engineering and scientific computing with Scilab. Springer Science & Business Media.
  47. ^ Thanki, R. M., & Kothari, A. M. (2019). Digital image processing using SCILAB. Springer International Publishing.
  48. ^ "ICCS - International Conference on Computational Science". Retrieved 2022-01-21.
  49. ^ Sloot, Peter; Coveney, Peter; Dongarra, Jack (2010). "Redirecting". Journal of Computational Science. 1 (1): 3–4. doi:10.1016/j.jocs.2010.04.003.
  50. ^ Seidel, Edward; Wing, Jeannette M. (2010). "Redirecting". Journal of Computational Science. 1 (1): 1–2. doi:10.1016/j.jocs.2010.04.004. S2CID 211478325.
  51. ^ Sloot, Peter M.A. (2010). "Computational science: A kaleidoscopic view into science". Journal of Computational Science. 1 (4): 189. doi:10.1016/j.jocs.2010.11.001.
  52. ^ "Announcing the Journal of Open Research Software - a software metajournal". software.ac.uk. Retrieved 2021-12-31.
  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.
  54. ^ "Computational Sciences and Informatics, PhD | GMU College of Science". science.gmu.edu. Retrieved 2024-04-24.
  55. ^ "SCIS | Welcome to Jawaharlal Nehru University". www.jnu.ac.in. from the original on 2013-03-10.
  56. ^ . www.jnu.ac.in. Archived from the original on 7 February 2020. Retrieved 31 December 2021.

Additional sources edit

  • E. Gallopoulos and A. Sameh, "CSE: Content and Product". IEEE Computational Science and Engineering Magazine, 4(2):39–43 (1997)
  • G. Hager and G. Wellein, Introduction to High Performance Computing for Scientists and Engineers, Chapman and Hall (2010)
  • A.K. Hartmann, , World Scientific (2009)
  • Journal Computational Methods in Science and Technology (open access), Polish Academy of Sciences
  • Journal Computational Science and Discovery, Institute of Physics
  • R.H. Landau, C.C. Bordeianu, and M. Jose Paez, A Survey of Computational Physics: Introductory Computational Science, Princeton University Press (2008)

External links edit

  • Journal of Computational Science
  • The Journal of Open Research Software
  • The National Center for Computational Science at Oak Ridge National Laboratory

computational, science, confused, with, computer, science, this, article, multiple, issues, please, help, improve, discuss, these, issues, talk, page, learn, when, remove, these, template, messages, this, article, needs, additional, citations, verification, pl. Not to be confused with computer science This article has multiple issues Please help improve it or discuss these issues on the talk page Learn how and when to remove these template messages This article needs additional citations for verification Please help improve this article by adding citations to reliable sources Unsourced material may be challenged and removed Find sources Computational science news newspapers books scholar JSTOR December 2021 Learn how and when to remove this template message This article contains text that is written in a promotional tone Please help improve it by removing promotional language and inappropriate external links and by adding encyclopedic text written from a neutral point of view December 2021 Learn how and when to remove this template message This article may be confusing or unclear to readers In particular the article does not describe the idea of computational science at a concrete level Please help clarify the article There might be a discussion about this on the talk page December 2021 Learn how and when to remove this template message Learn how and when to remove this template message 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 Algorithms numerical and non numerical mathematical models computational models and computer simulations developed to solve sciences e g physical biological and social engineering and humanities problems Computer hardware that develops and optimizes the advanced system hardware firmware networking and data management components needed to solve computationally demanding problems The computing infrastructure that supports both the science and engineering problem solving and the developmental computer and information science 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 Contents 1 The computational scientist 2 Applications of computational science 2 1 Predictive computational science 2 2 Urban complex systems 2 3 Computational finance 2 4 Computational biology 2 5 Complex systems theory 2 6 Computational science and engineering 3 Methods and algorithms 4 Conferences and journals 5 Education 5 1 Subfields 6 See also 7 References 8 Additional sources 9 External linksThe computational scientist edit nbsp 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 editProblem domains for computational science scientific computing include Predictive computational science edit 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 lt Nf lt N2 11 Urban complex systems edit 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 edit Main article 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 edit Main article 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 edit Main article Complex systems 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 edit Main article Computational 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 been described by whom as the third mode of discovery next to theory and experimentation 12 In many fields which computer simulation is integral and therefore essential to business and research Computer simulation provides the capability to enter fields which 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 editAlgorithms and mathematical methods used in computational science are varied Commonly applied methods include Computer algebra 13 14 15 16 including symbolic computation in fields such as statistics equation solving algebra calculus geometry linear algebra tensor analysis multilinear algebra optimization Numerical analysis 17 18 19 20 including Computing derivatives by finite differences Application of Taylor series as convergent and asymptotic series Computing derivatives by Automatic differentiation AD Finite element method for solving PDEs 21 22 High order difference approximations via Taylor series and Richardson extrapolation Methods of integration 23 on a uniform mesh rectangle rule also called midpoint rule trapezoid rule Simpson s rule Runge Kutta methods for solving ordinary differential equations Newton s method 24 Discrete Fourier transform Monte Carlo methods 25 26 Numerical linear algebra 27 28 29 including decompositions and eigenvalue algorithms Linear programming 30 31 Branch and cut Branch and bound Molecular dynamics Car Parrinello molecular dynamics Space mapping Time stepping methods for dynamical systems 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 editIn 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 Education editAt 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 Subfields edit Bioinformatics Car Parrinello molecular dynamics Cheminformatics Chemometrics Computational archaeology Computational astrophysics Computational biology Computational chemistry Computational materials science Computational economics Computational electromagnetics Computational engineering Computational finance Computational fluid dynamics Computational forensics Computational geophysics Computational history Computational informatics Computational intelligence Computational law Computational linguistics Computational mathematics Computational mechanics Computational neuroscience Computational particle physics Computational physics Computational sociology Computational statistics Computational sustainability Computer algebra Computer simulation Financial modeling Geographic information science Geographic information system GIS High performance computing Machine learning Network analysis Neuroinformatics Numerical linear algebra Numerical weather prediction Pattern recognition Scientific visualization SimulationSee also edit nbsp Science portal nbsp Mathematics portal Computational science and engineering Modeling and simulation Comparison of computer algebra systems Differentiable programming List of molecular modeling software List of numerical analysis software List of statistical packages Timeline of scientific computing Simulated reality Extensions for Scientific Computation XSC References edit Nonweiler T R 1986 Computational Mathematics An Introduction to Numerical Approximation John Wiley and Sons Graduate Education for Computational Science and Engineering Siam org Society for Industrial and Applied Mathematics SIAM website accessed Feb 2013 Siegler Bernard 1976 Theory of Modeling and Simulation Cellier Francois 1990 Continuous System Modelling Minski Marvin 1965 Models Minds Machines 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 Humphreys Paul Extending ourselves Computational science empiricism and scientific method Oxford University Press 2004 Gelfert Axel 2016 How to do science with models A philosophical primer Cham Springer 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 Oden J T Babuska I and Faghihi D 2017 Predictive computational science Computer predictions in the presence of uncertainty Encyclopedia of Computational Mechanics Second Edition pp 1 26 Szabo B Actis R and Rusk D Validation of notch sensitivity factors Journal of Verification Validation and Uncertainty Quantification 4 011004 2019 Computational Science and Engineering Program Graduate Student Handbook PDF cseprograms gatech edu September 2009 Archived from the original PDF on 2014 10 14 Retrieved 2017 08 26 Von Zur Gathen J amp Gerhard J 2013 Modern computer algebra Cambridge University Press Geddes K O Czapor S R amp Labahn G 1992 Algorithms for computer algebra Springer Science amp Business Media Albrecht R 2012 Computer algebra symbolic and algebraic computation Vol 4 Springer Science amp Business Media Mignotte M 2012 Mathematics for computer algebra Springer Science amp Business Media Stoer J amp Bulirsch R 2013 Introduction to numerical analysis Springer Science amp Business Media Conte S D amp De Boor C 2017 Elementary numerical analysis an algorithmic approach Society for Industrial and Applied Mathematics Greenspan D 2018 Numerical Analysis CRC Press Linz P 2019 Theoretical numerical analysis Courier Dover Publications Brenner S amp Scott R 2007 The mathematical theory of finite element methods Vol 15 Springer Science amp Business Media Oden J T amp Reddy J N 2012 An introduction to the mathematical theory of finite elements Courier Corporation Davis P J amp Rabinowitz P 2007 Methods of numerical integration Courier Corporation Peter Deuflhard Newton Methods for Nonlinear Problems Affine Invariance and Adaptive Algorithms Second printed edition Series Computational Mathematics 35 Springer 2006 Hammersley J 2013 Monte carlo methods Springer Science amp Business Media Kalos M H amp Whitlock P A 2009 Monte carlo methods John Wiley amp Sons Demmel J W 1997 Applied numerical linear algebra SIAM Ciarlet P G Miara B amp Thomas J M 1989 Introduction to numerical linear algebra and optimization Cambridge University Press Trefethen Lloyd Bau III David 1997 Numerical Linear Algebra 1st ed Philadelphia SIAM Vanderbei R J 2015 Linear programming Heidelberg Springer Gass S I 2003 Linear programming methods and applications Courier Corporation a b c Phillips Lee 2014 05 07 Scientific computing s future Can any coding language top a 1950s behemoth Ars Technica Retrieved 2016 03 08 a b Landau Rubin 2014 05 07 A First Course in Scientific Computing PDF Princeton University Retrieved 2016 03 08 Mathematica 6 Archived 2011 01 13 at the Wayback Machine Scientific Computing World May 2007 Maeder R E 1991 Programming in mathematica Addison Wesley Longman Publishing Co Inc Stephen Wolfram 1999 The MATHEMATICA book version 4 Cambridge University Press Shaw W T amp Tigg J 1993 Applied Mathematica getting started getting it done Addison Wesley Longman Publishing Co Inc Marasco A amp Romano A 2001 Scientific Computing with Mathematica Mathematical Problems for Ordinary Differential Equations with a CD ROM Springer Science amp Business Media Quarteroni A Saleri F amp Gervasio P 2006 Scientific computing with MATLAB and Octave Berlin Springer Gander W amp Hrebicek J Eds 2011 Solving problems in scientific computing using Maple and Matlab Springer Science amp Business Media Barnes B amp Fulford G R 2011 Mathematical modelling with case studies a differential equations approach using Maple and MATLAB Chapman and Hall CRC Jones E Oliphant T amp Peterson P 2001 SciPy Open source scientific tools for Python Bressert E 2012 SciPy and NumPy an overview for developers O Reilly Media Inc Blanco Silva F J 2013 Learning SciPy for numerical and scientific computing Packt Publishing Ltd Ihaka R amp Gentleman R 1996 R a language for data analysis and graphics Journal of computational and graphical statistics 5 3 299 314 Bunks C Chancelier J P Delebecque F Goursat M Nikoukhah R amp Steer S 2012 Engineering and scientific computing with Scilab Springer Science amp Business Media Thanki R M amp Kothari A M 2019 Digital image processing using SCILAB Springer International Publishing ICCS International Conference on Computational Science Retrieved 2022 01 21 Sloot Peter Coveney Peter Dongarra Jack 2010 Redirecting Journal of Computational Science 1 1 3 4 doi 10 1016 j jocs 2010 04 003 Seidel Edward Wing Jeannette M 2010 Redirecting Journal of Computational Science 1 1 1 2 doi 10 1016 j jocs 2010 04 004 S2CID 211478325 Sloot Peter M A 2010 Computational science A kaleidoscopic view into science Journal of Computational Science 1 4 189 doi 10 1016 j jocs 2010 11 001 Announcing the Journal of Open Research Software a software metajournal software ac uk Retrieved 2021 12 31 Rougier Nicolas P Hinsen Konrad Alexandre Frederic Arildsen Thomas Barba Lorena A Benureau Fabien C Y Brown C Titus Buyl Pierre de Caglayan Ozan Davison Andrew P Delsuc Marc Andre Detorakis Georgios Diem Alexandra K Drix Damien Enel Pierre Girard Benoit 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 Timothee 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 Computational Sciences and Informatics PhD GMU College of Science science gmu edu Retrieved 2024 04 24 SCIS Welcome to Jawaharlal Nehru University www jnu ac in Archived from the original on 2013 03 10 SCIS Program of Study Welcome to Jawaharlal Nehru University www jnu ac in Archived from the original on 7 February 2020 Retrieved 31 December 2021 Additional sources editE Gallopoulos and A Sameh CSE Content and Product IEEE Computational Science and Engineering Magazine 4 2 39 43 1997 G Hager and G Wellein Introduction to High Performance Computing for Scientists and Engineers Chapman and Hall 2010 A K Hartmann Practical Guide to Computer Simulations World Scientific 2009 Journal Computational Methods in Science and Technology open access Polish Academy of Sciences Journal Computational Science and Discovery Institute of Physics R H Landau C C Bordeianu and M Jose Paez A Survey of Computational Physics Introductory Computational Science Princeton University Press 2008 External links edit nbsp Wikiversity has learning resources about Scientific computing nbsp Wikimedia Commons has media related to Computational science Journal of Computational Science The Journal of Open Research Software The National Center for Computational Science at Oak Ridge National Laboratory Retrieved from https en wikipedia org w index php title Computational science amp oldid 1220594423, wikipedia, wiki, book, books, library,

article

, read, download, free, free download, mp3, video, mp4, 3gp, jpg, jpeg, gif, png, picture, music, song, movie, book, game, games.