Process model inversion in the data-driven engineering context for improved parameter sensitivities

HIGHLIGHTS

• who: Subiksha Selvarajan and collaborators from the Automation and Computer Sciences Department, Harz University of Applied Sciences, Friedrichstr57-59, Wernigerode, Germany have published the research work: Process Model Inversion in the Data-Driven Engineering Context for Improved Parameter Sensitivities, in the : Processes 2022, 10, 1764. of /2022/
• what: The authors provide some concluding remarks in Section 5. On that note, the model input is used to define an input-based loss function followed again by a model parameter adaptation step (Equation ) realized with a numerical optimization routine. This work shows that the input least squares . . .
  

   

  

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