Bayesian Multi-Source Modeling for Sparse Data Prediction
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Solution Overview
Problem
Complex engineering systems with stringent performance, environmental, and cost requirements face challenges in designing derivative systems due to sparse data availability, leading to inadequate predictive capabilities and decision-making processes.
Innovation Solution
A Bayesian multi-source modeling approach is developed, where models are built independently for each legacy dataset, accounting for discrepancies and model validity as a function of input space, using Bayesian Hybrid Modeling to combine legacy data and create a predictive model for new systems with sparse data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If new experiments are conducted to understand and design the new derivative design, then predictive capability is improved, but experimental cost and time increase
Solution Approach 1:
The patent applies preliminary action by using legacy data from existing systems to pre-establish predictive models before conducting new experiments. The system leverages historical data from legacy systems to create initial predictive capabilities, thereby reducing the time and cost required for new experiments to validate derivative designs. This allows engineers to make informed decisions about new designs using pre-existing knowledge and data patterns.
2Measurement precision
If new experiments are conducted to understand and design the new derivative design, then predictive capability is improved, but experimental cost increases
Solution Approach 1:
The patent applies copying by creating virtual replicas of legacy systems through data-driven models. Instead of physically conducting expensive new experiments, the system creates digital copies of legacy system behavior using historical data, allowing predictive analysis to be performed computationally at minimal cost. This virtual copying enables extensive testing and validation of derivative designs without the financial burden of physical experimentation.
3Device complexity
If sparse data from new design is used to build model, then model complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent applies merging by combining sparse data from new designs with abundant legacy data from existing systems. The system integrates these multiple data sources into a unified predictive model, where the legacy data compensates for the sparsity of new design data. This merging approach maintains relatively simple model structure while significantly improving predictive accuracy through the combined information from both new and legacy systems.
4Measurement precision
If legacy data is leveraged to improve predictive capability, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the predictive modeling task into separate modules: one for processing legacy data and another for incorporating new design data. The system segments the data processing workflow to handle legacy and new data independently before integrating them, which simplifies the overall model architecture. This modular segmentation allows the system to leverage legacy data for improved predictive accuracy without creating an overly complex integrated model structure.
Data Source
AI summary
A method for estimating a crack propagation rate includes receiving a first dataset for a new material design, the new material design including crack growth rate data, receiving second and third datasets for a plurality of different legacy systems associated with an existing material design, determining a legacy model for each of the plurality of different legacy systems based on the respective second and third datasets for each of the plurality of different legacy systems and the first dataset for the new material design, the legacy model based on a validity of each legacy system, the validity determined using a legacy model likelihood validity and a predictive uncertainty model validity, calculating a model weight to associate with each of the determined legacy models, and determining a first multi-source model for new data for the new material design based on the model weight.