LM Uncertainty Quantification for Automated Surrogate Model Selection
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Solution Overview
Problem
Current uncertainty quantification systems require user judgment for selecting input variables and lack automated methods to balance model errors and uncertainties, leading to inefficiencies and potential biases in model predictions.
Innovation Solution
An automated uncertainty quantification system that builds surrogate models using dual objective pareto optimal selection and Bayesian optimization, incorporating statistical inference and active learning to refine input data and select the most accurate models, reducing user bias and improving model efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated model selection and data refinement are implemented, then model accuracy and reliability are improved, but device complexity and computational resources increase
Solution Approach 1:
The system performs automated model selection, hyperparameter optimization, and data refinement without human intervention. The Bayesian optimization algorithm autonomously selects surrogate models and refines input data, while the uncertainty quantification framework automatically evaluates and compares model performances, enabling the system to serve itself in achieving high reliability predictions
Solution Approach 2:
The patent replaces manual user judgment and selection processes with computational algorithms. Bayesian optimization substitutes human expertise in model selection, while automated uncertainty quantification replaces manual error analysis, transforming subjective human processes into objective computational procedures that improve reliability
2Manufacturing precision
If user judgment and manual selection are used, then ease of operation is maintained, but model accuracy and objectivity deteriorate due to potential biases
Solution Approach 1:
The system replaces manual user judgment with automated Bayesian optimization and uncertainty quantification algorithms. These computational methods objectively evaluate model performances and select optimal configurations without human bias, thereby improving prediction accuracy while maintaining operational simplicity through automated workflows
Solution Approach 2:
The uncertainty quantification framework provides continuous feedback on model prediction reliability and error margins. This feedback mechanism allows the system to automatically adjust and refine model selections based on measured performance, improving accuracy through iterative optimization while requiring minimal user intervention
3Measurement precision
If comprehensive uncertainty quantification is performed, then measurement precision is improved, but loss of time and computational resources increase
Solution Approach 1:
The system performs preliminary uncertainty quantification during the model selection phase using Bayesian optimization. By estimating uncertainty margins early in the process, the system can prioritize models with lower predicted uncertainties, avoiding exhaustive analysis of all possible models and thereby reducing computational time while maintaining measurement precision
Data Source
AI summary
According to an embodiment, a computer-implemented method for operating a system for uncertainty quantification (UQ) of imperial data, a simulation of a mathematical model or for testing a technical system includes the following steps: (i) defining simulation output parameters and an accuracy range; (ii) uploading simulation output data and configuration file; (iii) searching for and applying a variety of surrogate models via automation; (iv) determining the reliability of the selected model; (v) providing a report; and (vi) using the validated model to generate new data points.