Computational Mechanics Workbench

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9616764 Sussman, Gerald J. The goal of this research is to create the architectural and algorithmic foundations for intelligent analysis and interpretation of massive numerical datasets. The aim is to provide the technology to reduce the complexity in the analysis of specific engineering problems, and at the same time use these specific problems as a focus to develop general principles that advance the frontiers of Artificial Intelligence. Smart data interpretation programs must exploit visual recognition and symbolic abstraction capabilities. Tracking a multiplicity of interacting, deforming objects might well be too taxing for the human visual system. It is both necessary and exciting to endow our computers with the capability to draw pictures for themselves to see and use what they saw to guide their own future actions. AI programs that integrate 'reasoning with images" with symbolic computations have been implemented to solve problems in several applications areas, such as dynamical system analysis, controller design, mechanism kinematics. This paradigm can be expected to be integral part of future engineering analysis and design environments. A computational environment will be provided for fluid dynamicists to efficiently extract structures and isolate interaction patterns form simulated or experimental turbulence data, and to perform control experiments on structure interactions. The objectives are : (1) to cut post-processing time of CFD data by one to two order of magnitude, (2) to provide symbolic descriptions of data for summarization and comparison, and (3) to develop symbolic and geometric techniques to encode coherent structures, their evolution, and interactions rules. ***