Tzvetan T. Drashansky

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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)
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)
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)
We present a paradigm for simulating complex systems which involve multiple physical phenomena and complicated geometries. The computational process uses cooperating agents to subdivide the physical object into components of simple geometric shapes modeled by a single problem solving environment (PSE). PSEs are viewed as agents which solve the PDE on each(More)
In this paper we describe a new approach to the pure functional programming which is based on the use of a local area network and external files. This approach essentially increases the efficiency and the field of the possible application of the functional programs. Our concepts are implemented in the framework of a FP-like functional language named FP*(More)
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