17540722. INTELLIGENT CALIBRATION OF SYSTEMS OF EQUATIONS WITH A KNOWLEDGE SEEDED VARIATIONAL INFERENCE FRAMEWORK simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
INTELLIGENT CALIBRATION OF SYSTEMS OF EQUATIONS WITH A KNOWLEDGE SEEDED VARIATIONAL INFERENCE FRAMEWORK
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Julian Bertram Kuehnert of Nairobi (KE)
Sekou Lionel Remy of Nairobi (KE)
Charles Muchiri Wachira of Karatina (KE)
Catherine H. Crawford of Bedford NH (US)
INTELLIGENT CALIBRATION OF SYSTEMS OF EQUATIONS WITH A KNOWLEDGE SEEDED VARIATIONAL INFERENCE FRAMEWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 17540722 titled 'INTELLIGENT CALIBRATION OF SYSTEMS OF EQUATIONS WITH A KNOWLEDGE SEEDED VARIATIONAL INFERENCE FRAMEWORK
Simplified Explanation
Abstract: A patent application describes a method for solving modeling problems by searching a database of prior calibrated models and using the information from similar problems to calibrate the received modeling problem. The calibrated modeling problem is continuously monitored for accuracy and can be recalibrated until a desired performance criterion is achieved. The calibrated modeling problem is then stored in the database for future reference.
Bullet Points:
- Method for solving modeling problems by searching a database of prior calibrated models.
- Identifying a similar problem with features similar to the received modeling problem.
- Calibrating the modeling problem using information from the identified similar problem.
- Monitoring the accuracy of the calibrated modeling problem.
- Recalibrating the modeling problem until a performance criterion is met.
- Storing the calibrated modeling problem in the database for future use.
Potential Applications:
- This technology can be applied in various fields that require modeling and calibration, such as engineering, finance, and data analysis.
- It can be used in predictive modeling to improve the accuracy of predictions and forecasts.
- It can assist in optimizing complex systems by calibrating models based on similar problems.
Problems Solved:
- The method solves the problem of calibrating modeling problems by utilizing prior calibrated models and information from similar problems.
- It addresses the challenge of achieving accurate modeling results by continuously monitoring and recalibrating the modeling problem.
Benefits:
- The method improves the accuracy of modeling problems by leveraging prior calibrated models and similar problem information.
- It saves time and effort by utilizing existing calibrated models instead of starting from scratch.
- The continuous monitoring and recalibration ensure that the modeling problem meets the desired performance criterion.
- Storing the calibrated modeling problem in a database allows for easy retrieval and future use.
Original Abstract Submitted
A modeling problem can be received. A database of prior calibrated models can be searched to identify a similar problem having features similar to the received modeling problem. The modeling problem can be calibrated using information of the identified similar problem. The accuracy of calibrated modeling problem can be monitored. The modeling problem can be recalibrated until a performance criterion is met. Calibrated modeling problem can be stored in the database.
