Digital Twin Calibration Using State-Wide Bayesian Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital twin calibration techniques for scientific instruments are limited to simplistic parameters with easily understood interdependencies and fail to address the parameter ambiguity problem, leading to inaccurate calibration and simulation of complex parameters with unknown or intricate interactions.
Innovation Solution
Implementing a state-wide Bayesian filter that performs recursive Bayesian updates on all parameters of the digital twin using a single, common observable across multiple calibration iterations, incrementally adjusting the parametric state to synchronize with the physical state of the scientific instrument.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing digital twin calibration techniques are used for scientific instruments with complex parameters, then the calibration process is simple and easy to implement, but the calibration accuracy is poor due to parameter ambiguity and intricate interactions
Solution Approach 1:
The patent segments the calibration process into multiple iterative cycles, where each cycle performs localized Bayesian updates on specific parameter subsets rather than attempting to calibrate all parameters simultaneously. This divides the complex calibration task into manageable segments that can be processed incrementally, improving accuracy while maintaining computational feasibility.
Solution Approach 2:
The patent implements a dynamic calibration approach where the Bayesian filter continuously adapts and updates parameter estimates as new observational data becomes available. The calibration process is not static but evolves iteratively, adjusting parameter uncertainties and refining estimates based on incoming measurements, which resolves the contradiction between handling complex parameter interactions and maintaining process simplicity.
2Measurement precision
If a state-wide Bayesian filter is implemented to accurately calibrate complex parameters, then calibration accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the parametric state into multiple parameter subsets that can be updated independently in parallel. Rather than performing a single comprehensive Bayesian update on all parameters simultaneously, the system divides parameters into groups that can be processed separately, reducing the computational burden of each individual update while maintaining overall calibration accuracy through iterative refinement.
Solution Approach 2:
The patent applies partial Bayesian updates to specific parameter subsets in each iteration rather than performing complete state-wide updates. This partial action approach focuses computational resources on the most uncertain or critical parameters at each step, achieving sufficient calibration accuracy without the full computational overhead of updating all parameters simultaneously in every iteration.
3Measurement precision
If multiple different observables are used for different parameter subsets, then each parameter can be calibrated with appropriate measurements, but the calibration process becomes complex and difficult to manage
Solution Approach 1:
The patent implements a universal Bayesian filtering framework that can handle multiple different observable types within a single unified computational structure. The same Bayesian filter algorithm processes diverse observables (different measurement types, formats, and uncertainties) through a common mathematical framework, eliminating the need for separate calibration procedures for different parameter subsets and significantly simplifying operational complexity.
Solution Approach 2:
The patent introduces the Bayesian filter as an intermediary computational layer that mediates between diverse observables and the parametric state. This intermediary transforms various types of measurements into a unified parameter estimation process, allowing different observables to be processed through a single consistent mechanism rather than requiring direct, complex mappings from each observable type to its corresponding parameters.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Systems or techniques are provided for facilitating improved digital twin calibration for scientific instruments. In various embodiments, a system can synchronize, via execution of a state-wide Bayesian filter, a parametric state of a digital twin with a physical state of a scientific instrument. In various instances, the state-wide Bayesian filter can comprise a set of calibration iterations, each of which can comprise a Bayesian update to an entirety of the parametric state based on an iteration-common observable that is exhibitable by the scientific instrument and simulatable by the digital twin. In various cases, the scientific instrument can be a charged-particle microscope, the parametric state of the digital twin can be an aberration coefficient vector of the charged-particle microscope, and the iteration-common observable can be a Fourier transform of a convergent beam electron diffraction pattern of an amorphous carbon specimen captured by the charged-particle microscope.