Rapid Accurate Estimator Machine for System Model Approximation
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Solution Overview
Problem
Traditional methods for calculating summary statistics of complex systems, such as Monte Carlo simulations, are costly in terms of computational resources and time due to the large number of input and operational variables and their interdependencies, making it inefficient for rapid approximation of system models.
Innovation Solution
The development of a Rapid Accurate Estimator Machine (RACE) that iteratively generates equations of increasing complexity to approximate a System Under Study (SUS) by prioritizing dominant contributing variables and using analytical integration techniques to estimate mean, variance, and other summary statistics, thereby avoiding processing of non-dominant contributors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte Carlo simulation is used to calculate summary statistics, then accuracy of system model approximation is improved, but computational cost and time increase significantly
Solution Approach 1:
The patent segments the system model into multiple components or subsystems that can be approximated separately. By dividing the complex system into smaller manageable parts, the computational burden is reduced while maintaining overall model accuracy. Each segment can be processed independently, allowing for faster computation compared to traditional Monte Carlo simulation of the entire system.
Solution Approach 2:
The patent extracts and identifies the most influential variables and parameters from the complex system model. By focusing computational resources on only the critical factors that significantly impact the output, the method achieves accurate approximations without processing all variables, thereby reducing computational time and resources while maintaining measurement precision.
2Measurement precision
If traditional system modeling methods are used with large numbers of input and operational variables, then model accuracy is improved, but computational resources and hardware experimental cost increase
Solution Approach 1:
The patent applies local quality by assigning different levels of processing detail to different parts of the system model based on their importance. Critical subsystems or variables that significantly affect the output are modeled with high precision, while less influential components are approximated with simpler models. This selective approach maintains overall model accuracy while significantly reducing the total computational resources required.
3Reliability
If complex models with many variables and interdependencies are created, then reliability of system analysis is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent extracts and isolates the key variables and interdependencies that truly drive system behavior. By removing redundant or less influential variables from the model, the complexity is reduced while the reliability of the analysis is maintained through focus on the critical factors. This extraction process creates a simplified model that is easier to process and analyze.
Solution Approach 2:
The patent performs preliminary analysis to identify and rank variables by their impact on system output before building the full model. This preliminary action allows the modeler to structure the analysis in a way that prioritizes reliable estimation of critical parameters first, reducing the need for complex processing of all variables simultaneously and thereby reducing device complexity while maintaining analysis reliability.
Data Source
AI summary
Systems, methods, apparatus and mechanisms that iteratively generate one or more equations (conforming to one or more functions or functions types) of increasing complexity (e.g., degree or order) to provide one or more corresponding approximations of a physical System Under Study (SUS), where each approximation or “fit” provides additional information about the SUS and where the various equations may be summed to provide a final “fit” having an adequate level of accuracy.