COMPUTACIÓN CIENTÍFICA

1. COMPUTACIÓN CIENTÍFICA

2. Definición: de Wikipedia • Scientific computing (or computational science) is the field of study concerned with constructing mathematical models and numerical solution techniques and using computers to analyze and solve scientific and engineering problems. In practical use, it is typically the application of computer simulation and other forms of computation to problems in various scientific disciplines. • The field is distinct from computer science (the mathematical study of computation, computers and information processing). It is also different from theory and 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.

3. Alcances • Scientists and engineers develop computer programs, application software, that model systems being studied and run these programs with various sets of input parameters. Typically, these models require massive amounts of calculations (usually floating-point) and are often executed on supercomputers (computer that leads the world in terms of processing capacity, particularly speed of calculation) or distributed computing platforms. • Numerical analysis is an important technique used in scientific computing. Numerical simulations have different objectives depending on the nature of the task being simulated

4. Objetivos concretos en otras disciplinas distintas de computación: • Reconstruct and understand known events (e.g., earthquake, tsunamis and other natural disasters). • Optimise known scenarios (e.g., technical and manufacturing processes). • Predict future or unobserved situations (e.g., weather, sub-atomic particle behaviour).

5. Algorithms and mathematical methodsused in scientific computing are varied. Commonly applied methods include: • Numerical analysis • Application of Taylor series as convergent and asymptotic series • Computing derivatives by Automatic differentiation (AD) • Computing derivatives by finite differences • High order difference approximations via Taylor series and Richardson extrapolation • Methods for integration on a uniform mesh: rectangle rule, trapezoid rule, midpoint rule, Simpson's rule • Runge Kutta method for solving ordinary differential equations • Monte Carlo methods • Numerical Linear Algebra • Computing the LU factors by Gaussian elimination • Choleski factorizations • Discrete Fourier transform and applications. • Newton's method • Time stepping methods for dynamical systems

6. Algorithms and mathematical methodsused in scientific computing are varied. Commonly applied methods include: • Numerical analysis • Application of Taylor series as convergent and asymptotic series • Computing derivatives by Automatic differentiation (AD) • Computing derivatives by finite differences • High order difference approximations via Taylor series and Richardson extrapolation • Methods for integration on a uniform mesh: rectangle rule, trapezoid rule, midpoint rule, Simpson's rule • Runge Kutta method for solving ordinary differential equations • Monte Carlo methods • Numerical Linear Algebra • Computing the LU factors by Gaussian elimination • Choleski factorizations • Discrete Fourier transform and applications. • Newton's method • Time stepping methods for dynamical systems

7. Lenguajes • Programming languages commonly used for the more mathematical aspects of scientific computing applications include Fortran, MATLAB, GNU Octave, Num-Python, Sci-Python and PDL. • The more computationally-intensive aspects of scientific computing will often utilize some variation of C or Fortran.

8. Computational science application programs often model real-world changing conditions, such as: • weather, • air flow 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.

9. Contexto • 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 computers in different ways to advance the state-of-the-art in their respective applied disciplines in physics, chemistry or engineering. Scientific computing has increasingly also impacted on other areas including economics, biology and medicine. • Computational science is now commonly considered a third mode of science, complementing and adding to experimentation/observation and theory. This thesis has been propounded by many, including Stephen Wolfram (most notably in his book A New Kind of Science), and Jürgen Schmidhuber

10. Scientific Computing World Scientific Computing World is Europe's premier magazine devoted to the computing and information technology needs of those in science, engineering, technology and medicine. It reports on research, development, testing, and laboratory analysis (including QA/QC). Particularly known for its authoritative reviews of maths/stats software, the magazine also covers Laboratory Information Management Systems (LIMS), and computing in chemistry, physics, and life sciences. SIAM Journal on Scientific Computing The SIAM Journal on Scientific Computing contains research articles on numerical methods and techniques for scientific computation. Papers address computational issues relevant to the solution of scientific or engineering problems and generally include computational results demonstrating the effectiveness of the proposed techniques. Publication Information Computational Results for Scientific and Engineering Problems ISSN Print: 1064-8275 Electronic: 1095-7197 Revistas

11. SCIENTIFIC COMPUTING: An Introductory Survey, Second Editionby Michael T. Heath, published by McGraw-Hill, New York, 2002 Table of Contents • Scientific Computing • Systems of Linear Equations • Linear Least Squares • Eigenvalue Problems • Nonlinear Equations • Optimization • Interpolation • Numerical Integration and Differentiation • Initial Value Problems for Ordinary Differential Equations • Boundary Value Problems for Ordinary Differential Equations • Partial Differential Equations • Fast Fourier Transform • Random Numbers and Stochastic Simulation • Bibliography

12. Educación • Scientific computation is most often studied through an applied mathematics or computer science program, or within a standard mathematics, sciences, or engineering program. However, there are increasingly many B.S. programs in computational science, notably at SUNY-Brockport (Capital University of Ohio offers a well-known minor program in computational studies that was one of the first NSF-funded programs in the field). There are also master's degrees in computational science or scientific computation, including the Sloan Foundation's professional science master's programs . The University of Waterloo in Canada offers a B.Math in computational mathematics and a Master's program in scientific computing as well. Some schools also offer the Ph.D. in computational science, computational engineering, computational science and engineering, or scientific computation, such as the University of Texas at Austin Institute for Computational Engineering and Sciences (ICES), McMaster University School of Computational Engineering and Science, Purdue University, and UCSB. At some institutions a specialization in scientific computation can be earned as a "minor" within another program (which may be at varying levels). • There are also programs in areas such as computational physics, computational chemistry, etc.

13. Campos relacionados • Bioinformatics • Cheminformatics • Chemometrics • Computational chemistry • Computational biology • Computational mechanics • Computational physics • Computational Electromagnetics • Computational fluid dynamics • Computational economics • Environmental simulations • Financial modeling • Geographic information system (GIS) • Numerical weather prediction

14. Chemometrics • Chemometrics is the science of relating measurements made on a chemical system or process to the state of the system via application of mathematical or statistical methods. • Chemometric research spans a wide area of different methods which can be applied in chemistry. There are techniques for collecting good data (optimization of experimental parameters, design of experiments, calibration, signal processing) and for getting information from these data (statistics, pattern recognition, modeling, structure-property-relationship estimations). • Chemometrics tries to build a bridge between the methods and their application in chemistry.

15. Computational Chemistry • Computational chemistry is a branch of chemistry that uses the results of theoretical chemistry incorporated into efficient computer programs to calculate the structures and properties of molecules and solids, applying these programs to real chemical problems. Examples of such properties are structure (i.e. the expected positions of the constituent atoms), energy and interaction energy, charges, dipoles and higher multipole moments, vibrational frequencies, reactivity or other spectroscopic quantitities, and cross sections for collision with other particles. The term computational chemistry is also sometimes used to cover any of the areas of science that overlap between computer science and chemistry. Electronic configuration theory is the largest subdiscipline of computational chemistry.

16. Cheminformatics • Cheminformatics is the use of computer and informational techniques, applied to a range of problems in the field of chemistry. Also known as chemoinformatics and chemical informatics. These in silico techniques are used in pharmaceutical companies in the process of drug discovery.

17. Bioinformatics • Bioinformatics and computational biology involve the use of techniques including applied mathematics, informatics, statistics, computer science, artificial intelligence, chemistry and biochemistry to solve biological problems usually on the molecular level. Research in computational biology often overlaps with systems biology. Major research efforts in the field include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, and the modeling of evolution.

18. Information Technology • IT is concerned with the use of technology in managing and processing information, especially in large organizations. • In particular, IT deals with the use of electroniccomputers and computer software to convert, store, protect, process, transmit, and retrieve information. For that reason, computer professionals are often called IT specialists or Business Process Consultants, and the division of a company or university that deals with software technology is often called the IT department. Other names for the latter are information services (IS) or management information services (MIS), managed service providers (MSP).

19. Information and Communication Technology • In the United Kingdom education system, information technology was formally integrated into the school curriculum when the National Curriculum was devised. It was quickly realised that the work covered was useful in all subjects. With the arrival of the Internet and the broadband connections to all schools, the application of IT knowledge, skills and understanding in all subjects became a reality. This change in emphasis has resulted in a change of name from Information Technology to Information and Communication Technology (ICT). ICT in Education can be understood as the application of digital equipment to all aspects of teaching and learning. It is present in almost all schools and is of growing influence. • The growth of use of Information and Communications Technology and its tools in the field of Education has seen tremendous growth in the recent past. Technology has entered the classroom in a big way to become part of the teaching and learning process.