Subproject: Decomposition methods for mixed-integer optimal control (A05)
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Funding: German Research Foundation (DFG), Sonderforschungsbereich (SFB), Collaborative Research Center (CRC) Transregio 154
Abstract: In the project we study domain decomposition approaches for optimal control problems using the example of gas transport networks. Our main goal is to couple the space-time-domain decomposition method from the second phase with machine learning and mixed-integer programming techniques. To this end, we develop an interlinked data-driven and physics informed algorithm called NeTI (Network Tearing and Interconnection) that combines mixed-integer nonlinear programming, learning of surrogate models, and graph decomposition strategies.
Adrian Göß, Doctoral Researcher
adrian.goess@utn.de, +49 911 9274-1606