Multi-Acquisition Bayesian Optimization for High-Cost Digital Twins
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Solution Overview
Problem
Existing Bayesian optimization techniques for selecting high-cost computational objective functions in industrial processes are inefficient, sensitive to bias, and data-inefficient due to the disposal of data from each execution, lacking a comprehensive strategy to balance exploration and exploitation.
Innovation Solution
A data-efficient multi-acquisition strategy that employs multiple Bayesian acquisition functions in parallel, utilizing a surrogate model to generate a POD surrogate model and implement a multi-fidelity Gaussian process regression, balancing exploration and exploitation to optimize input parameters efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Bayesian optimization is implemented with a single acquisition function, then the optimization process can be executed, but the system becomes highly sensitive to bias and less efficient in finding optimal solutions
Solution Approach 1:
The patent combines multiple acquisition functions (expected improvement, probability of improvement, and model variance) into a unified multi-acquisition framework. This merging allows the system to leverage complementary strengths of different acquisition functions, reducing sensitivity to bias in any single function while improving optimization reliability through diversified exploration strategies.
2Productivity
If data from each execution is disposed of in traditional Bayesian optimization, then the optimization process can proceed, but the system becomes data-inefficient and requires more computational evaluations
Solution Approach 1:
The patent implements a data retention and reuse mechanism where data from previous computational model executions is preserved and integrated into the multi-acquisition framework. Instead of discarding intermediate data, the system recovers and leverages this information across multiple acquisition functions, improving data efficiency and reducing the total number of expensive computational evaluations needed.
3Measurement precision
If multiple acquisition functions are used in parallel, then data efficiency and optimization accuracy improve, but the computational complexity and resource requirements increase
Solution Approach 1:
The patent segments the optimization process by assigning different acquisition functions to handle specific aspects: expected improvement for exploitation, probability of improvement for balanced exploration-exploitation, and model variance for pure exploration. This segmentation allows each function to specialize in its strength while the integrated framework manages computational resources efficiently, balancing accuracy improvement with complexity control.
Data Source
AI summary
A method of optimizing parameters for an industrial process is described, along with media and systems, using a digital twin, physics based model and multiple types of acquisition functions. Output data from the model is analyzed by multiple types of Bayesian acquisition functions, such as an expected improvement acquisition function and a model variance acquisition function. The different acquisition functions tune better parameters, and then then model is re-run in parallel for each to output more data. The data from one acquisition function's run of the model may be co-mingled with data from the other acquisition function's run of the model such that the acquisition functions' exploration and exploitation of the parameter space are intertwined, thus achieving a more globally optimal solution than using just one type of Bayesian acquisition function.