Panel Discussion from the 2007 SIAM Conference on Computational Science and Engineering
As one of the primary funding agencies for research efforts in Computational Science and Engineering, the National Science Foundation requested that the CS&E community come together to discuss current and future research paths of importance to continued CS&E developments.
Unlike other, more focused SIAM activity groups, Computational Science and Engineering is inherently multi-disciplinary, combining applied mathematics and computer science technologies with such diverse application areas as biology, fluid dynamics, materials science, chemistry, astrophysics, geophysics, and climate modeling. Due to this inter-disciplinary nature, the focus of this panel discussion was not on research topics for each of these individual application areas, but instead on the math and CS technologies that cut across these disciplines, enabling the kind of computational science that each member of this community is working toward.
As a result, in this session we explored research directions in such topics as multi-scale modeling, data management, scalable algorithms, inverse problems, and modeling of coupled multi-physics systems. While there are certainly many other areas relevant to CS&E, such as uncertainty quantification and visualization, it is believed that the interests of this group represent a number of items dear to our own applications.
Below, you will find links to the panelists' websites and their talks. A SIAM news article will be published combining the discussion into a coherent document from which discussion can be continued.
- Daniel Reynolds, University of California, San Diego
Panelists and their slides
- Terence Critchlow, Lawrence Livermore National Laboratory, data management
- Eric De Sturler, Virginia Polytechnic Institute & State University, inverse problems
- Robert Falgout, Lawrence Livermore National Laboratory, scalable algorithms for linear systems
- Michael Holst, University of California, San Diego, multiscale modeling and spatially adaptive methods
- John Shadid, Sandia National Laboratories, large-scale simulation of coupled multi-physics systems
SIAM News Article
Related links with enabling technology software (alphabetical)