Tzvetan T. Drashansky

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Problem solving using complex mathematical models of the physical phenomena requires expert knowledge in a variety of elds of computer science, such as parallel computing and numerical methods. This often makes application scientists, who have the domain expertise to devise the mathematical models, unable to use the power of High Performance Computing (HPC)(More)
Problem solving using complex mathematical models of the physical phenomena requires expert knowledge in a variety of fields of computer science, such as parallel computing and numerical methods. This often makes application scientists, who have the domain expertise to devise the mathematical models, unable to use the power of High Performance Computing(More)
Ideally, a designer would change some aspect of the engine and then run a simulation to see how the change affects the performance, cost, durability, and so forth. Such a simple approach will be infeasible for the foreseeable future because the complete simulation of an engine design requires days, weeks or even years on petaflops class computing systems.(More)
Recent and anticipated technological advances in wireless computing will permit scientists and engineers to do experimental and computational modeling \anywhere" and \any time." In the SciencePad project our aim is to develop such \ubiquitous" problem solving environments (UPSE). The main objective of this paper is to address the architectural design of(More)
The National Information Infrastructure (NII) that will evolve in the 1990's and beyond will impact many institutions of life. These include the way we learn and do science, self{caring, access to civil/information infrastructure systems & services, and management & control of manufacturing processes. The future scenario for the NII assumes wireless(More)
Recent and anticipated technological advances in wireless computing will permit users to compute ubiquitously, \anywhere" and \any time." However, mobile platforms are unlikely to have the computational resources to solve even moderately complex problems that users routinely solve on static workstations today. In the SciencePad project our aim is to develop(More)
The process of prototyping is part of every scientific inquiry, product design, and learning activity. Economic realities require fast, accurate prototyping using knowledge and computational models from multiple disciplines in science and engineering [l, 3, 15, 47, 491. Thus rapid multidisciplinary problem solving or 40 1 ADVANCES IN COMPIITERS, VOL. 46(More)
Systems with interacting agents are now being proposed to solve many problems grouped together under the \distributed problem solving" umbrella. For such systems to work properly, it is necessary that agents learn from their environment and adapt their behaviour accordingly. We investigate such systems in the context of scientiic computing. The physical(More)
THE CENTRAL ARGUMENT OF THIS ARTICLE IS THAT AGENT-BASED computing provides important advantages for scientific computing. We present our ideas in the context of a particular application, the simulation of gas turbine engines. This application is typical in that it involves an enormously complex device of great economic importance, one whose design is(More)