A toolbox and a record for scientific model development

Scientific computation can benefit from software tools that facilitate construction of computational models, control the application of models, and aid in revising models to handle new situations. Existing environments for scientific programming provide only limited means of handling these tasks. This paper describes a two pronged approach for handling these tasks: (1) designing a 'Model Development Toolbox' that includes a basic set of model constructing operations; and (2) designing a 'Model Development Record' that is automatically generated during model construction. The record is subsequently exploited by tools that control the application of scientific models and revise models to handle new situations. Our two pronged approach is motivated by our belief that the model development toolbox and record should be highly interdependent. In particular, a suitable model development record can be constructed only when models are developed using a well defined set of operations. We expect this research to facilitate rapid development of new scientific computational models, to help ensure appropriate use of such models and to facilitate sharing of such models among working computational scientists. We are testing this approach by extending SIGMA, and existing knowledge-based scientific software design tool

Intelligent Model Selection for Hillclimbing Search in Computer-Aided Design

Models of physical systems can differ according to computational cost, accuracy and precision, among other things. Depending on the problem solving task at hand, different models will be appropriate. Several investigators have recently developed methods of automatically selecting among multiple models of physical systems. Our research is novel in that we are developing model selection techniques specifically suited to computer-aided design. Our approach is based on the idea that artifact performance models for computer-aided design should be chosen in light of the design decisions they are required to support. We have developed a technique called "Gradient Magnitude Model Selection" (GMMS), which embodies this principle. GMMS operates in the context of a hillclimbing search process. It selects the simplest model that meets the needs of the hillclimbing algorithm in which it operates. We are using the domain of sailing yacht design as a testbed for this research. We have implemented GMM..

Intelligent Model Selection for Hillclimbing Search in Computer-Aided Design

Models of physical systems can dier according to computational cost, accuracy and precision, among other things. Depending on the problem solving task at hand, different models will be appropriate. Several investigators have recently developed methods of automatically selecting among multiple models of physical systems. Our research is novel in that we are developing model selection techniques specifically suited to computer-aided design. Our approach is based on the idea that artifact performance models for computer-aided design should be chosen in light of the design decisions they are required to support. We have developed a technique called Gradient Magnitude Model Selection" (GMMS), which embodies this principle. GMMS operates in the context of a hill climbing search process. It selects the simplest model that meets the needs of the hill climbing algorithm in which it operates. We are using the domain of sailing yacht design as a testbed for this research. We have implemented GMMS and used it in hill climbing search to decide between a computationally expensive potential- ow program and an algebraic approximation to analyze the performance of sailing yachts. Experimental tests show that GMMS makes the design process faster than it would be if the most expensive model were used for all design evaluations. GMMS achieves this performance improvement with little or no sacrifice in the quality of the resulting design.Technical report CAP-TR-1