Automatisierte Anbindung von Simulations- und Optimierungssoftware zur parallelen Lösung inverser Problemklassen.

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Abstract

This work introduces a novel extension of the Python software emphEnvironment for Combining Optimization and Simulation Software (EFCOSS). The extension addresses the solution of optimization problems of different types. Various problem instances that demonstrate the feasibility of this approach in new practical application scenarios include geothermal engineering and material science. In a more general context, EFCOSS enables to investigate the questions of which parameter values best fit a given computer model to measurements from real-world experiments, how should such experiments be designed with minimal uncertainty, which computer models should be used for a specific task, and how can the efficiency of such investigations be improved by using simpler models. These questions are addressed by employing techniques from parameter estimation, space mapping, optimal experimental design, and model identification that are implemented and brought together in EFCOSS. To this end, the internal structure of EFCOSS had to be redesigned completely to introduce a new software architecture based on standard Python packages. This new architecture allows for multiple optimization problems, objective functions, and simulation routines to be used within a single application. Simulation models of geothermal reservoirs in the regions of Perth in Australia and Tuscany in Italy are used as illustrating examples of optimal experimental design to find the location of new borehole sites that introduce low uncertainty in the parameter estimation. Furthermore, new parameter estimation problems are solved using space-mapping algorithms. Model identification is applied to metal-plasticity models to investigate different kinds of models. By combining automatic differentiation, parallelization, and reuse of intermediate results, the serial runtimes of the described problems are reduced from several weeks to minutes.