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Steven F. Ashby SIAG-CSE Chair March 24, 2003

Steven F. Ashby SIAG-CSE Chair March 24, 2003. Computational Science & Engineering meeting national needs. Computational science challenges arise in a variety of applications. Computational science is emerging as its own discipline

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Steven F. Ashby SIAG-CSE Chair March 24, 2003

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  1. Steven F. AshbySIAG-CSE ChairMarch 24, 2003 Computational Science & Engineeringmeeting national needs

  2. Computational science challenges arise in a variety of applications • Computational science is emerging as its own discipline • Simulation is becoming a peer to theory and experiment in the process of scientific discovery • Integration is the key • domain science expert • applied mathematician • computer scientist Turbulence Fusion Materials Biology Lasers Environment

  3. Computational Applied Math Domain Science Science Computer Science Engineering = + Biology Physics Chemistry Engineering Environmental Science and Engineering Applications Applied Mathand CS Math sparse linear solvers nonlinear equations differential eqns multilevel methods AMR techniques optimization eigenproblems CS data management data mining visualization program’g models languages, OS compilers, debuggers architectural issues Computational scientists bring applied mathematics and computer science capabilities to bear on challenging problems in science and engineering Computational Science & Engineering is a team effort!

  4. Our focus has been on solving PDEs on increasingly finer meshes • Traditional supercomputing applications involve the solution of a PDE on a computational grid • computational fluid dynamics • oil reservoir and groundwater management • stockpile stewardship • ICF and MFE applications • Bigger machines and smarter algorithms have allowed more realistic simulations • Moore’s Law and massively parallel computers have provided unprecedented computing power • scalable algorithms enable large-scale simulations

  5. Imagine the future of computational science by looking at today’s challenges • Consider the process of scientific simulation • software development • problem definition and simulation setup • data analysis and understanding • There has been no equivalent of Moore’s Law for how we develop our software • Increasingly complex simulations often require months to set up and months to analyze the results

  6. Investment needed in several areas (illustrative, not exhaustive) • Multi-level methods for multi-scale problems • Rapid problem setup tools (mesh generation and discretization methods for complex geometries) • Flexible software frameworks and interoperable s/w components for rapid application development • Computer architectures & performance optimization • Information exploitation (data management, image analysis, info/data visualization, data mining) • Systems engineering to integrate simulation, sensors, and info analysis into a decision support capability • Discrete simulation (scenario planning) • Validation and Verification (coupling to experiments)

  7. This workshop is about shaping CS&E programs for federal funding agencies • We should focus on how CSE can benefit the nation • enhancing national & homeland security • promoting economic vitality and energy security • improving human health • We need to emphasize the multi-disciplinary nature of CS&E and its track record in delivering! • distinguish ourselves from constituent disciplines • need to do a better job of getting the word out! • Think big: $250M, multi-agency initiative!

  8. We have long-time and natural partners in the federal government • DOE has been long-time leader in CS&E • ASCI re-invigorated supercomputing • Office of Science is championing the cause with its successful SciDAC initiative • NSF has long invested in IT and CS, and is beginning to think more about CS&E • DHS has pressing needs for help in simulation and information fusion • NIH should be a bigger player than it is, but there are serious cultural obstacles

  9. SIAM Activity Group promotes Computational Science & Engineering • SIAG-CSE established in Dec 2000 and already is largest SIAG with 800 members • Foster collaborations among applied mathematicians, computer scientists, domain scientists and engineers • Promote and facilitate Computational Science and Engineering as an academic discipline • Promote simulation as a peer to theory and experiment in the process of scientific discovery • Has sponsored two successful conferences • http://www.siam.org/siags/siagcse.htm

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