Design Space Exploration with Quantitative Pruning and Ranking
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Solution Overview
Problem
Current simulation software for multi-domain systems lacks efficient methods for generating and ranking component models that accurately represent the functional operation of these systems, leading to suboptimal design solutions.
Innovation Solution
A processing system and method that utilize design space expansion, pruning, and ranking algorithms to select and visualize component model solutions based on correspondence between simulated components and functional models, ensuring behaviors are consistent and optimized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If design space expansion is performed to generate all possible component model solutions, then completeness of solution coverage is improved, but computational complexity and processing time increase exponentially
Solution Approach 1:
The patent applies preliminary action by performing pruning operations early in the design space exploration process, before complete enumeration of all solutions. The system identifies and eliminates inconsistent component models early based on behavioral constraints, preventing exponential growth of the solution space while maintaining completeness of valid solutions.
Solution Approach 2:
The patent segments the design space exploration into distinct phases: generation of component model solutions, pruning of inconsistent solutions, and ranking of remaining solutions. This segmentation allows the system to handle each phase separately with appropriate algorithms, reducing overall computational complexity while maintaining solution completeness.
2Measurement precision
If all component model solutions are generated and analyzed, then accuracy of behavior matching is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary pruning of component model solutions that cannot possibly match the desired behavioral constraints, before conducting detailed accuracy analysis. This preliminary filtering reduces the number of solutions requiring full behavioral analysis, maintaining accuracy while reducing processing time.
Solution Approach 2:
The patent replaces exhaustive mechanical enumeration of all solutions with an intelligent pruning algorithm that uses behavioral constraints to eliminate inconsistent solutions. This substitution transforms the problem from brute-force analysis to constraint-based filtering, significantly reducing processing time while maintaining accuracy.
3Manufacturing precision
If comprehensive ranking of all component model solutions is performed, then quality of design selection is improved, but computational overhead increases
Solution Approach 1:
The system performs preliminary pruning to eliminate inconsistent solutions before ranking, reducing the computational overhead of the ranking process. By removing invalid solutions early, the ranking algorithm operates on a smaller, more manageable set of candidates, maintaining selection quality while reducing computational burden.
Solution Approach 2:
The patent segments the evaluation process into pruning (filtering inconsistent solutions) and ranking (evaluating remaining solutions). This segmentation allows each process to be optimized independently, with pruning handling completeness and ranking handling quality, reducing overall computational overhead.
4Reliability
If strict behavioral consistency filtering is applied, then reliability of component model solutions is improved, but number of viable solutions decreases
Solution Approach 1:
The system applies strict behavioral consistency filtering as a preliminary pruning step to eliminate fundamentally inconsistent solutions. By performing this filtering early, the system ensures reliability of remaining solutions while maintaining a sufficient quantity for selection, as the filtering removes only truly invalid candidates.
Data Source
AI summary
A system is provided that facilitates design space exploration with quantitative pruning and ranking. The system may determine a collection of component model solutions corresponding to a functional model with functional model ports for a system to be produced. The component model solutions are comprised of simulated components selected from a component library based at least in part on correspondence between component ports of the simulated components and the functional model ports of the functional model. The system may select a subset of the component model solutions from the collection, which have behaviors determined for each component model solution that are consistent with behaviors determined for the functional model. The system may determine rankings for the component model solutions of the subset relative to each other based on a comparison of behaviors for each component model solution to each other and/or to the behaviors determined for the functional model.