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Computational science, also known as scientific computing or scientific computation (SC), is a rapidly growing branch of applied computer science and mathematics that uses advanced computing capabilities to understand and solve complex problems. It is an area of science which spans many disciplines, but at its core, it involves the development of models and simulations to understand natural systems.

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 experiment which are the traditional forms of science and engineering. The scientific computing approach is to gain understanding, mainly through the analysis of mathematical models implemented on computers. Scientists and engineers develop computer programs, 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/or 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. Actually the science which deals with the Computer Modeling and Simulation of any physical objects and phenomena by high programming language and software and hardware is known as Computer Simulation.

The computational scientist

Ways to study a system

The term computational scientist is used to describe someone skilled in scientific computing. This 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, complementing and adding to experimentation/observation and theory (see image on the right).[2] Here, we define 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 conceptualise the system containing these problems
  • design a framework of algorithms suitable for studying this system: the simulation
  • choose a suitable computing infrastructure (parallel computing/grid computing/supercomputers)
  • hereby, maximising the computational power of the simulation
  • assessing to what level the output of the simulation resembles the systems: the model is validated
  • adjust the conceptualisation of the system accordingly
  • repeat cycle until a suitable level of validation is obtained: the computational scientists trusts that the simulation generates adequately realistic results for the system, under the studied conditions

In fact, substantial effort in computational sciences has been devoted to the development of algorithms, the efficient implementation in programming languages, and validation of 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 do we 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.

Applications of computational science

Problem domains for computational science/scientific computing include:

Urban complex systems

In 2015, over half the world's population live in cities. By the middle of the 21st century, it is estimated that 75% of the world's population will be urban. This urban growth is focused in the urban populations of developing countries where city dwellers will more than double, increasing from 2.5 billion in 2009 to almost 5.2 billion in 2050. Cities are massive 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 requires 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 urbanisation.

Computational finance

Main article: Computational finance

In today's 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 these 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 has clearly demonstrated the role of cascading effects 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 modelled simultaneously and at different interconnected scales.

Computational biology

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 biomineralisation 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, biomineralisation 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 to understand such systems is by developing a multi-scale model of the system.

Complex systems theory

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.

Computational science in engineering

Main article: Computational engineering

Computational science and engineering (CSE) is a relatively new 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 as the "third mode of discovery" (next to theory and experimentation).[10] 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).

Methods and algorithms

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

Both historically and today, Fortran remains popular for most applications of scientific computing.[30][31] Other programming languages and computer algebra systems commonly used for the more mathematical aspects of scientific computing applications include GNU Octave, Haskell,[30] Julia,[30] Maple,[31] Mathematica,[32][33][34][35][36] MATLAB,[37][38][39] Python (with third-party SciPy library[40][41][42]), Perl (with third-party PDL library),[citation needed] R,[43] Scilab,[44][45] 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.

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 equations that describe how the system operates; and then repeat the process to calculate the next state.

Conferences and journals

In the year 2001, the International Conference on Computational Science (ICCS) was first organised. Since then it has been organised yearly. ICCS is an A-rank conference in CORE classification.

The international Journal of Computational Science published its first issue in May 2010.[46][47][48] A new initiative was launched in 2012, the Journal of Open Research Software.[49] In 2015, ReScience[50] dedicated to the replication of computational results has been started on GitHub.

Education

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 programme master program computational science at the University of Amsterdam and the Vrije Universiteit in computational science was first offered in 2004. In this programme, 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 theory that will give a firm basis for the analysis of complex systems;
  • learn to analyse the results of simulations in a virtual laboratory using advanced numerical algorithms.

George Mason University was one of the early pioneers first offering a multidisciplinary doctorate Ph.D program in Computational Sciences and Informatics in 1992 that focused on a number of specialty areas including bioinformatics, computational chemistry, earth systems and global changes, computational mathematics, computational physics, space sciences, and computational statistics

School of Computational and Integrative Sciences, Jawaharlal Nehru University (erstwhile School of Information Technology[51]) also offers a vibrant master's science program for computational science with two specialities namely- Computational Biology and Complex Systems.[52]

See also

References

  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. ^ "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.
  11. ^ Von Zur Gathen, J., & Gerhard, J. (2013). Modern computer algebra. Cambridge University Press.
  12. ^ Geddes, K. O., Czapor, S. R., & Labahn, G. (1992). Algorithms for computer algebra. Springer Science & Business Media.
  13. ^ Albrecht, R. (2012). Computer algebra: symbolic and algebraic computation (Vol. 4). Springer Science & Business Media.
  14. ^ Mignotte, M. (2012). Mathematics for computer algebra. Springer Science & Business Media.
  15. ^ Stoer, J., & Bulirsch, R. (2013). Introduction to numerical analysis. Springer Science & Business Media.
  16. ^ Conte, S. D., & De Boor, C. (2017). Elementary numerical analysis: an algorithmic approach. Society for Industrial and Applied Mathematics.
  17. ^ Greenspan, D. (2018). Numerical Analysis. CRC Press.
  18. ^ Linz, P. (2019). Theoretical numerical analysis. Courier Dover Publications.
  19. ^ Brenner, S., & Scott, R. (2007). The mathematical theory of finite element methods (Vol. 15). Springer Science & Business Media.
  20. ^ Oden, J. T., & Reddy, J. N. (2012). An introduction to the mathematical theory of finite elements. Courier Corporation.
  21. ^ Davis, P. J., & Rabinowitz, P. (2007). Methods of numerical integration. Courier Corporation.
  22. ^ Peter Deuflhard, Newton Methods for Nonlinear Problems. Affine Invariance and Adaptive Algorithms, Second printed edition. Series Computational Mathematics 35, Springer (2006)
  23. ^ Hammersley, J. (2013). Monte carlo methods. Springer Science & Business Media.
  24. ^ Kalos, M. H., & Whitlock, P. A. (2009). Monte carlo methods. John Wiley & Sons.
  25. ^ Demmel, J. W. (1997). Applied numerical linear algebra. SIAM.
  26. ^ Ciarlet, P. G., Miara, B., & Thomas, J. M. (1989). Introduction to numerical linear algebra and optimization. Cambridge University Press.
  27. ^ Trefethen, Lloyd; Bau III, David (1997). Numerical Linear Algebra (1st ed.). Philadelphia: SIAM.
  28. ^ Vanderbei, R. J. (2015). Linear programming. Heidelberg: Springer.
  29. ^ Gass, S. I. (2003). Linear programming: methods and applications. Courier Corporation.
  30. ^ 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.
  31. ^ a b Landau, Rubin (2014-05-07). "A First Course in Scientific Computing" (PDF). Princeton University. Retrieved 2016-03-08.
  32. ^ Mathematica 6 Scientific Computing World, May 2007
  33. ^ Maeder, R. E. (1991). Programming in mathematica. Addison-Wesley Longman Publishing Co., Inc..
  34. ^ Stephen Wolfram. (1999). The MATHEMATICA® book, version 4. Cambridge University Press.
  35. ^ Shaw, W. T., & Tigg, J. (1993). Applied Mathematica: getting started, getting it done. Addison-Wesley Longman Publishing Co., Inc..
  36. ^ Marasco, A., & Romano, A. (2001). Scientific Computing with Mathematica: Mathematical Problems for Ordinary Differential Equations; with a CD-ROM. Springer Science & Business Media.
  37. ^ Quarteroni, A., Saleri, F., & Gervasio, P. (2006). Scientific computing with MATLAB and Octave. Berlin: Springer.
  38. ^ Gander, W., & Hrebicek, J. (Eds.). (2011). Solving problems in scientific computing using Maple and Matlab®. Springer Science & Business Media.
  39. ^ Barnes, B., & Fulford, G. R. (2011). Mathematical modelling with case studies: a differential equations approach using Maple and MATLAB. Chapman and Hall/CRC.
  40. ^ Jones, E., Oliphant, T., & Peterson, P. (2001). SciPy: Open source scientific tools for Python.
  41. ^ Bressert, E. (2012). SciPy and NumPy: an overview for developers. " O'Reilly Media, Inc.".
  42. ^ Blanco-Silva, F. J. (2013). Learning SciPy for numerical and scientific computing. Packt Publishing Ltd.
  43. ^ Ihaka, R., & Gentleman, R. (1996). R: a language for data analysis and graphics. Journal of computational and graphical statistics, 5(3), 299-314.
  44. ^ Bunks, C., Chancelier, J. P., Delebecque, F., Goursat, M., Nikoukhah, R., & Steer, S. (2012). Engineering and scientific computing with Scilab. Springer Science & Business Media.
  45. ^ Thanki, R. M., & Kothari, A. M. (2019). Digital image processing using SCILAB. Springer International Publishing.
  46. ^ Sloot, Peter; Coveney, Peter; Dongarra, Jack (2010). "Redirecting". Journal of Computational Science. 1 (1): 3–4. doi:10.1016/j.jocs.2010.04.003.
  47. ^ Seidel, Edward; Wing, Jeannette M. (2010). "Redirecting". Journal of Computational Science. 1 (1): 1–2. doi:10.1016/j.jocs.2010.04.004.
  48. ^ 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.
  49. ^ The Journal of Open Research Software ; announced at software.ac.uk/blog/2012-03-23-announcing-journal-open-research-software-software-metajournal
  50. ^ 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.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  51. ^ "SCIS | Welcome to Jawaharlal Nehru University".
  52. ^ "SCIS: Program of Study | Welcome to Jawaharlal Nehru University".

Additional sources

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