Patents by Inventor Ashok Ramanath Bhakta

Ashok Ramanath Bhakta has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Combining machine learning with domain knowledge and first principles for modeling in the process industries
    Patent number: 11853032
    Abstract: Computer-based process modeling and simulation methods and systems combine first principles models and machine learning models to benefit where either model is lacking. In one example, input values (measurements) are adjusted by first principles techniques. A machine learning model of the chemical process of interest is trained on the adjusted values. In another example, a machine learning model represents the residual (delta) between a first principles model prediction and empirical data. Residual machine learning models correct physical phenomena predictions in a first principles model of the chemical process. In another example, a first principles simulation model uses the process input data and predictions of the machine learning model to generate simulated results of the chemical process.
    Type: Grant
    Filed: May 6, 2020
    Date of Patent: December 26, 2023
    Assignee: AspenTech Corporation
    Inventors: Willie K. C. Chan, Benjamin Fischer, Hernshann Chen, Ashok Ramanath Bhakta, Parham Mobed
  • Combining Machine Learning With Domain Knowledge And First Principles For Modeling In The Process Industries
    Publication number: 20200379442
    Abstract: Computer-based process modeling and simulation methods and systems combine first principles models and machine learning models to benefit where either model is lacking. In one example, input values (measurements) are adjusted by first principles techniques. A machine learning model of the chemical process of interest is trained on the adjusted values. In another example, a machine learning model represents the residual (delta) between a first principles model prediction and empirical data. Residual machine learning models correct physical phenomena predictions in a first principles model of the chemical process. In another example, a first principles simulation model uses the process input data and predictions of the machine learning model to generate simulated results of the chemical process.
    Type: Application
    Filed: May 6, 2020
    Publication date: December 3, 2020
    Inventors: Willie K. C. Chan, Benjamin Fischer, Hernshann Chen, Ashok Ramanath Bhakta, Parham Mobed