In recent literature, data science techniques have been used to accomplish a variety of tasks related to representing simulated data for control or estimation purposes2,3,4. Data science techniques have also been used to approximate optimal solutions to computationally difficult problems, such as the traveling salesman problem5. Rather than create a surrogate model to use with a typical optimization scheme, we will examine framing the problem as a parameter ‘correction’ endeavor. By doing so, it may be possible to retain all of the information present in a time series of error and leverage this information to very quickly go from a poor initial guess to a viable solution, after which time iterative optimization schemes can take over. The effectiveness of this approach (both in terms of efficiency and robustness) will be compared with the standard approaches. This method is inspired by DeepChess, a comparative deep neural network structure which plays successfully against world-class chess algorithms6.
Acknowledgments
The authors thank the United States Department of Energy (DOE) for the financial support for this work though the Advanced Research Projects Agency – Energy (ARPA-E) award #DEAR0000275, as well as the Clean Energy Institute (CEI) through the DIRECT program at the University of Washington. Part of the research has been supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Vehicle Technologies of the U. S. Department of Energy through the Advanced Battery Materials Research (BMR) Program (Battery500 Consortium).
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