18298727. SYMBOLIC MODEL DISCOVERY RECTIFICATION simplified abstract (International Business Machines Corporation)

SYMBOLIC MODEL DISCOVERY RECTIFICATION

Organization Name

International Business Machines Corporation

Inventor(s)

Lior Horesh of North Salem NY (US)

Cristina Cornelio of Kilchberg (CH)

Sanjeeb Dash of Croton on Hudson NY (US)

Joao P. Goncalves of Wappingers Falls NY (US)

Kenneth Lee Clarkson of Madison NJ (US)

Nimrod Megiddo of Palo Alto CA (US)

Bachir El Khadir of White plains NY (US)

Vernon Ralph Austel of Cortlandt Manor NY (US)

SYMBOLIC MODEL DISCOVERY RECTIFICATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18298727 titled 'SYMBOLIC MODEL DISCOVERY RECTIFICATION

• • Simplified Explanation:**

This patent application describes a method for refining a model that is initially mis-specified by utilizing data and constraints to generate partial expression trees and solve optimization problems to determine a more accurate symbolic model.

• • Key Features and Innovation:**
• Method for refining mis-specified symbolic models
• Utilizes data and constraints to generate partial expression trees
• Solves optimization problems to determine refined symbolic model

• • Potential Applications:**

This technology could be applied in various fields such as engineering, data analysis, and scientific research where accurate modeling is crucial.

• • Problems Solved:**

This technology addresses the issue of refining mis-specified symbolic models to improve accuracy and reliability in modeling processes.

• • Benefits:**
• Improved accuracy in symbolic modeling
• Enhanced understanding of processes or phenomena
• Increased reliability in model predictions

• • Commercial Applications:**

Potential commercial applications include software tools for data analysis, simulation software for engineering applications, and research tools for scientific modeling.

• • Questions about the Technology:**

1. How does this method improve upon traditional modeling techniques?

  - This method improves upon traditional modeling techniques by incorporating data and constraints to refine mis-specified symbolic models, leading to more accurate results.

2. What are the potential limitations of this technology in real-world applications?

  - The potential limitations of this technology may include computational complexity and the need for high-quality data inputs.

• • Frequently Updated Research:**

There may be ongoing research in the field of symbolic modeling and optimization techniques that could further enhance the capabilities of this technology.

Original Abstract Submitted

A method for obtaining a refined model given a mis-specified symbolic model. The method includes receiving a mis-specified symbolic model and data pertaining to a process or phenomenon corresponding to the mis-specified symbolic model; receiving one or more constraints; generating a plurality of partial expression trees based on the mis-specified symbolic model; solving an optimization problem for each of the partial expression trees; and determining a refined symbolic model of the mis-specified symbolic model based on results of the optimization problem for each partial expression tree.