PDE Solving Kernels and Systems for Multi-Physics Applications

Principal Investigators: Elias N. Houstis, John R. Rice

Research Assistants: T. Drashansky, S. Markus

Sponsor: ARPA

The predicted growth of computational power and network bandwidth suggests that computational modeling and experimentation will be one of the main tools in big and small science. In this scenario, computational modeling will shift from the current single physical component design to the design of a whole physical system with a large number of components that have different shapes, obey different physical laws and manufacturing constraints, and interact with each other through geometric and physical interfaces. The design of the engine requires that these different domain-specific analyses interact in order to find the final solution. The different domains share common parameters and interfaces but each has its own parameters and constraints. We refer to these multi-component based physical systems as multi-physics applications. The realization of the above scenario, which is expected to have significant impact in industry, education, and training, will require the development of new algorithmic strategies and software for managing the complexity and harvesting the power of the expected HPCC resources; it will require problem solving environments technology to support programming-in-the-large and reduce the overhead of HPCC computing. The goal of this research is to identify the framework for the numerical simulation of multi-physics applications and to develop the enabling theories and technologies needed to support and realize this framework in specific applications. See the PDEPACK and SciAgents project web pages for more information.