1 / 5

Expanding Horizons in Computational Science: Current Trends and Future Directions

Computational science traditionally focuses on hard sciences like physics, chemistry, and engineering. It involves mesh-based calculations and numerical simulations for verifying theories and enhancing predictive capabilities. Significant research areas include numerical algorithms and high-performance computing. The DOE SciDAC Program exemplifies successful application-focused initiatives in fields like climate and astrophysics. However, there's a pressing need for more interdisciplinary collaboration, exploring areas like biology and economics, and improving education in computational science to foster a new generation of experts.

tyra
Télécharger la présentation

Expanding Horizons in Computational Science: Current Trends and Future Directions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. What do we currently mean by Computational Science? • Traditionally focuses on the “hard sciences” and engineering • Physics, Chemistry, Mechanics, Aerospace, etc. • Mesh-based calculations, direct numerical simulations, optimization, etc. – modeling the scientific phenomenon directly • Used for verification of experiments, verification of theory, and, increasingly, predictive capabilities • Associated research: numerical algorithms, “big” hardware (computing, storage, networking) • Requires teams of experts – no single researcher can do it all

  2. One Example: DOE SciDAC Program • A program that works • “I’ve never been so happy as a computational scientist”, Tony Mezzacappa, TSI, March 11, 2003 • Targets specific Office of Science applications • Fusion, Accelerator Design, Basic Energy Sciences, Climate, Astrophysics, etc. • Unites these application scientists with ISICS • 3 Applied Mathematics (TOPS, APDEC, TSTT) • 4 Computer Science (CCA, PERC, Scalable Systems, Data Management) • ISICs are charged with collaborating with application teams • What’s missing from this program? How can it be improved? • The obvious: more applications, more CS, more math • Tighter ties with computer architecture development…. What else?

  3. Research Themes What new directions can/should be explored? • CS&E digging more deeply into the traditional areas • Multiscale modeling, hybrid solution techniques, etc. • CS&E moving into “nontraditional” areas • Biological sciences, economics, medicine, computational finance, etc. • Some traditional techniques, but also data mining, statistics,… • It’s not always about big hardware • What about… • English, Law, History, Archeology,…? • Almost no exposure in these areas – How can this be rectified? What can be accomplished if we turn our attention in these areas?

  4. Supporting CS&E What has worked? How do we go forward? • Large, goal-based initiatives have worked • Requires • Advances in applications, mathematics, and computer science • Integration of the three areas • An adequate reward system for those interactions • Examples • Grand Challenges, DOE SciDAC program • This is much easier at the labs than universities..

  5. Education Is the educational system “on track” for CS&E? • Many institutions are trying to understand where CS&E fits within their departmental structures • Computational science still traditionally done in the “discipline” departments • It’s difficult to find new hires that have been trained in all three aspects of computational science • Very few educational programs take a truly interdisciplinary tact • Typically 1 or 2 aspects considered • Of course, there are exceptions, e.g., TICAM, DOE CSGF • How do we increase the number of programs that are truly interdisciplinary? Can we? Should we? • How can we help stretch departmental budgets to accommodate cross-cutting curricula? What infrastructure is/can be provided at the national level from NSF or SIAM? • How do we encourage the exploration of new disciplines?

More Related