2008 DOE Summer School in Multiscale Mathematics and High Performance Computing
About
About the Northwest Consortium
The Northwest Consortium for Multiscale Mathematics and Applications, founded in 2005 with funding from U.S. Department of Energy (DOE), is a collaborative effort between Pacific Northwest National Laboratory, Washington State University and Oregon State University. Our mission is to address critical problems in multiscale modeling, to enhance modeling in engineering and science by bringing mathematical and computational tools to bear on practical problems, to create a continuous communication between relevant disciplines, and to enhance engineering and science education to meet the requirements of the 21st century.
About the DOE and Multiscale Mathematics
DOE sponsored three workshops in 2004 to consider the scientific needs and mathematical challenges for multiscale simulation. These meetings were structured to solicit advice from the engineering, mathematics, and scientific communities in developing a roadmap for future investments in multiscale mathematics. The scientists participating in the workshop included primarily natural scientists–physicists, chemists, geologists, biologists, as well as computational mathematicians and computer scientists. The number and location of the workshops was selected to maximize participation from a comprehensive cross section of the scientific community.
The recommendations to come out of this series of workshops support research, collaboration, training, and disciplinary infrastructure.
"Research will be necessary across the spectrum from theory to application: formalisms and frameworks, algorithm development and implementation in software, analysis metrics and tools, and demonstrations of applied principles within specific problem domains. Of particular interest will be: 1) mathematical bridges across levels of type and scale such as stochastic to deterministic, discrete to continuous, interscale coupling; 2) mathematically derived metrics for error, uncertainty, stability, and performance bounds; 3) software development including implementations of new algorithms and problem sets for benchmarking, the transfer of existing software to new problem domains, validation and verification.
Research opportunities should encourage individual efforts as well as highly interdisciplinary teams that partner university scientists with their counterparts in the national laboratories. To spur innovation, a few projects deemed to be high risk should be encouraged. To fully disseminate current knowledge, and to prepare for the rapid dissemination of future research results, multiscale mathematics will require communication conduits including workshops, conferences, travel within collaborative research teams, training programs for young scientists, and educational products such as textbooks."
