Lehrstuhl Informatik V Scientific Computing I Module 1: Introduction Miriam Mehl based on Slides by Michael Bader (Winter 09/10) Winter 2011/2012 Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 1
Lehrstuhl Informatik V Scientific Computing = Science + Computing? Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 2
Lehrstuhl Informatik V Scientific Computing = Science + Computing? Science on Computers?? Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 2
Lehrstuhl Informatik V Scientific Computing = Science + Computing? Science on Computers?? “Computational Science”??? Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 2
Lehrstuhl Informatik V A Short Look into Wikipedia . . . Not to be confused with computer science. Computational science (or scientific computing ) is the field of study concerned with • constructing mathematical models • and quantitative analysis techniques • and using computers to analyze and solve scientific, social scientific and engineering problems. [ . . . ]The scientific computing approach is to gain understanding , mainly through the analysis of mathematical models implemented on computers. [ . . . ]massive amounts of calculations (usually floating-point) and are often executed on supercomputers or distributed computing platforms. [Wikipedia, Sep 21, 2011] Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 3
Lehrstuhl Informatik V Part I An Interdisciplinary Discipline Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 4
Lehrstuhl Informatik V Gaining Scientific Knowledge The Classical Scientific Process 1. characterization • observation • quantification/measurement Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 5
Lehrstuhl Informatik V Gaining Scientific Knowledge The Classical Scientific Process 1. characterization • observation • quantification/measurement 2. hypothesis • theory • model Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 5
Lehrstuhl Informatik V Gaining Scientific Knowledge The Classical Scientific Process 1. characterization • observation • quantification/measurement 2. hypothesis • theory • model 3. prediction • consequences/logical deducation from hypothesis/model? Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 5
Lehrstuhl Informatik V Gaining Scientific Knowledge The Classical Scientific Process 1. characterization • observation • quantification/measurement 2. hypothesis • theory • model 3. prediction • consequences/logical deducation from hypothesis/model? 4. experiment • verification/falsification • discrepancies might lead to improved model Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 5
Lehrstuhl Informatik V Gaining Scientific Knowledge (2) Approaches to Science: 1. theoretical investigation • hypothesis / models • analytical calculations • Gedankenexperiments Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 6
Lehrstuhl Informatik V Gaining Scientific Knowledge (2) Approaches to Science: 1. theoretical investigation • hypothesis / models • analytical calculations • Gedankenexperiments 2. experimentation • build model scenarios • compare observations with predicted theoretical results Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 6
Lehrstuhl Informatik V Gaining Scientific Knowledge (2) Approaches to Science: 1. theoretical investigation • hypothesis / models Sience • analytical calculations • Gedankenexperiments Experiments 2. experimentation Theory • build model scenarios • compare observations with predicted theoretical results Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 6
Lehrstuhl Informatik V Gaining Scientific Knowledge (2) Approaches to Science: 1. theoretical investigation • hypothesis / models • analytical calculations • Gedankenexperiments Sience 2. experimentation Experiments Simulation • build model scenarios Theory • predict theoretical results and compare with outcome 3. simulation • Why would we need that? Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 7
Lehrstuhl Informatik V Drawbacks of Theory and Experiment Theoretical Investigation: • analytical solutions for simple scenarios, only, • models usually very complicated or even impossible to solve Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 8
Lehrstuhl Informatik V Drawbacks of Theory and Experiment Theoretical Investigation: • analytical solutions for simple scenarios, only, • models usually very complicated or even impossible to solve Experiments: • might be impossible to do • might be dangerous or unwelcome • might be very expensive Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 8
Lehrstuhl Informatik V The Scientific Process Revisited Scientific/Engineering Tasks: scientific/engineering tasks: Reality • understand processes (model) • verify/validate hypotheses/models Theory Solution (experiment) Validation • design and optimize (model or experiment) Experiment Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 9
Lehrstuhl Informatik V Where Simulation is Needed Replacing Analytical Solvers: • analytical solution impossible or hard to Reality compute • use numerical approximation instead Theory • application: validate a complex model Simulation Validation • understand processes • validate assumptions Experiment Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 10
Lehrstuhl Informatik V Where Simulation is Needed (2) Replacing Experiments: • analytical theoretical solution available Reality • replace experiments by simulation of a more detailed model Theory • application: develop a simple model Solution • neglecting non-relevant effects Validation • with reduced dimensionality Simulation • reduced-order models Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 11
Lehrstuhl Informatik V Where Simulation is Needed (3) Replacing Analytical Solvers and Experiments: • detailed and accurate mathematical model given • use simulation only Reality • requires real world scenario description Prediction • application: predict reality Simulation • (wheather/climate/earthquake/...) forecasts • design and optimization • uncertainty quantification Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 12
Lehrstuhl Informatik V Where Experiments are Impossible Astrophysics: • “life cycle” of stars, galaxies, . . . • motion of planets, asteroids, comets, . . . Geophysics: • displacement of the earth’s magnetic field • continental drift Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 13
Lehrstuhl Informatik V Where Experiments are Impossible Meteorology: • weather forecasts • tornado prediction Climate Research: • greenhouse effect • ocean currents (gulf stream, etc.) Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 14
Lehrstuhl Informatik V When There is No Second Try Stability of Buildings: • large span bridges or skyscrapers • consider wind loads, earthquakes, . . . Astronautics • flight path of space crafts or satellites • re-entry of space crafts Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 15
Lehrstuhl Informatik V Where Experiments have Harmful Side-effects Propagation of Pollutants: • pollutants in air, water, or soil • predict long-term behaviour Nuclear Research: • security of nuclear power plants • nuclear weapons Miriam Mehl based on Slides by Michael Bader (Winter 09/10): Scientific Computing I Module 1: Introduction, Winter 2011/2012 16
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