Neural Network Model Interpretability via Governing Equation Conversion
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Solution Overview
Problem
Existing data analysis technologies struggle to effectively describe the correlation between input data and output data, particularly for high-level AI models, and to identify physical characteristics of these models.
Innovation Solution
A data analysis apparatus and method that convert a portion of a neural network model into a governing equation, allowing for the analysis of data and the correlation between input and output data, while also enabling the identification of physical characteristics of the neural network model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-level AI model with many hidden layers is used for data analysis, then prediction accuracy is improved, but understanding the correlation between input and output data and identifying physical characteristics becomes difficult
Solution Approach 1:
The patent segments the neural network model into multiple components, including a physical characteristic module that extracts specific physical parameters (such as mass, damping coefficient, stiffness) from the network weights. This segmentation allows the complex high-level model to be analyzed in terms of interpretable physical characteristics while maintaining prediction accuracy.
Solution Approach 2:
The patent introduces an intermediary layer between the neural network computation and the final output that maps network activations to physical characteristic space. This intermediary enables the translation of abstract neural network operations into meaningful physical correlations, making the relationship between input and output data interpretable.
2Measurement precision
If a high-level AI model with many hidden layers is used for data analysis, then prediction accuracy is improved, but identifying physical characteristics of the model becomes difficult
Solution Approach 1:
The patent segments the neural network model into multiple components, including a physical characteristic module that extracts specific physical parameters (such as mass, damping coefficient, stiffness) from the network weights. This segmentation allows the complex high-level model to be analyzed in terms of interpretable physical characteristics while maintaining prediction accuracy.
Solution Approach 2:
The patent transforms the parameters of the neural network (weights and biases) into physical characteristic parameters through a mapping function. By changing the parameter representation from abstract numerical values to physically meaningful quantities, the model retains its predictive power while gaining interpretability in terms of physical characteristics.
Data Source
AI summary
A data analysis apparatus and a method thereof are provided. The data analysis apparatus includes a memory that stores a neural network model and a program instruction and a processor that executes the program instruction. The processor inputs at least one of a first dataset, a second dataset, or any combination thereof to the neural network model, in response to identifying the second dataset obtained during a first specified duration before a time point when the first dataset is obtained. The processor obtains a governing equation corresponding to a portion of a plurality of neurons included in the neural network model. The processor changes the portion of the plurality of neurons included in the neural network model to the governing equation. The processor obtains a third dataset for predicting data during a second specified duration after the time point, from the neural network model.