Bayesian Calibration of Computational Models for Object Performance
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Solution Overview
Problem
Existing methods for predicting the performance of physical objects are time-consuming and inaccurate due to divergence between mathematical physics models and real-world performance, leading to inefficiencies in testing and potential misclassification of samples.
Innovation Solution
Developing predictive models trained using ground-truth data and Bayesian techniques to account for uncertainty and measurement errors, utilizing linear regression and Markov Chain Monte Carlo models to improve accuracy in predicting object performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If mathematical physics models are used to predict object performance, then testing time is reduced, but prediction accuracy deteriorates due to divergence from actual tested performance
Solution Approach 1:
A Bayesian calibration model is introduced as an intermediary between the computational model and ground truth measurements. This calibration model learns the relationship between predicted values and actual measurements, correcting systematic biases and uncertainties without requiring changes to the underlying computational physics model. The calibration model acts as a mediator that translates model predictions into accurate performance predictions by accounting for model deficiencies through learned correction factors.
Solution Approach 2:
The invention changes the parameters of the predictive system by introducing calibration parameters (Bayesian priors and likelihoods) that are learned from data. Instead of modifying the complex computational physics model, the approach adjusts simpler calibration parameters that capture the discrepancy between model predictions and reality. These parameters are optimized to minimize the difference between predicted and measured values, thereby improving accuracy while maintaining computational efficiency.
2Measurement precision
If manual testing of each sample is performed to ensure accuracy, then prediction accuracy is improved, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The invention creates a calibrated computational copy of the physical testing process. Instead of physically testing each sample, the calibrated model generates virtual test results that accurately reflect actual performance. The Bayesian calibration ensures that this digital twin (computational model) faithfully reproduces the behavior of physical objects, enabling virtual testing to replace physical testing while maintaining accuracy and dramatically increasing throughput.
3Productivity
If computational models are used to minimize testing, then productivity is improved, but reliability deteriorates due to potential acceptance or rejection of samples that do not conform to specifications
Solution Approach 1:
The Bayesian calibration framework incorporates feedback from ground truth measurements to continuously improve prediction reliability. The calibration process uses measured data to update the model's understanding of its own uncertainties and biases. This feedback loop allows the model to learn from discrepancies between predictions and measurements, adjusting calibration parameters to reduce false acceptances and rejections, thereby improving the reliability of sample classification decisions.
Data Source
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AI summary
The present disclosure provides a processor-implemented method in one aspect, the processor-implemented method including: generating a training data set including a plurality of exemplars, each exemplar of the plurality of exemplars including ground-truth values for one or more properties of a sample of an object and corresponding predicted values for the one or more properties of the sample of the object, the corresponding predicted values for the one or more properties of the sample of the object being generated based on a computational model of the object; training a predictive model to predict the one or more properties of samples of the object based on the training data set; and predicting, using the predictive model, one or more properties of a new sample of the object.