Design Space Analyzer for Efficient Optimization
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Solution Overview
Problem
Conventional design optimization algorithms waste computational resources by exploring regions of the design space that contain few or no feasible designs, leading to inefficient and time-consuming processes.
Innovation Solution
A design space analyzer generates a parametric model, discretizes parameters, computes metrics for sample designs, and evaluates regions for feasibility criteria, modifying the design space to exclude infeasible areas, thereby focusing optimization on feasible designs and reducing computational waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional optimization algorithms explore the entire design space, then comprehensive design options are generated, but excessive computational resources are wasted on infeasible regions
Solution Approach 1:
The system performs preliminary analysis of the design space before running the full optimization algorithm. By evaluating feasibility criteria on sampled designs first, the system identifies and excludes infeasible regions in advance, preventing the optimization algorithm from wasting computational resources on them while still maintaining comprehensive exploration of feasible regions.
2Productivity
If the design space is filtered to exclude infeasible regions, then computational efficiency is improved, but the feasibility criteria must be accurately determined
Solution Approach 1:
The system applies feasibility criteria evaluation to only a subset of the design space (sampled designs) rather than the entire design space. This partial application of feasibility analysis provides sufficient information to guide the optimization algorithm efficiently without requiring exhaustive analysis of all possible designs, thus balancing efficiency and complexity.
Data Source
AI summary
A design space analyzer generates a parametric model associated with a design problem. The design space analyzer then discretizes various parameters associated with the model and generates a plurality of sample designs using different combinations of discretized parameters. The design space analyzer also computes one or more metrics for each sample design. In this fashion, the design space analyzer generates a coarse approximation of the design space associated with the design problem. The design space analyzer then evaluates portions of that approximation, at both global and local scales, to identify portions of the design space that meet certain feasibility criteria. Finally, the design space analyzer modifies the design space to facilitate more efficient exploration during optimization.