Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process
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DOI: 10.1080/03036758.2020.1863237
Abstract: ABSTRACT This paper derives predictive reduced-order models for rocket engine combustion dynamics via Operator Inference, a scientific machine learning approach that blends data-driven learning with physics-based modelling. The non-intrusive nature of the approach enables variable… read more here.
Keywords: reduced order; operator; order models; operator inference ... See more keywords