Interactive Constraint Modulation for Pareto Design Space Exploration
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Solution Overview
Problem
Current automated design search (ADS) systems require tedious manual processes to modify and reconfigure constraints, limiting the flexibility and efficiency of design space exploration.
Innovation Solution
A system and method for dynamically adjusting and updating design constraints through user interaction with a graphical interface, allowing for automated filtering and visualization of potential Pareto solutions based on constraint relaxation inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual processes are used to modify and reconfigure constraints in ADS systems, then constraint modification is possible, but the process becomes tedious and inefficient
Solution Approach 1:
The system implements dynamic constraint modification through a graphical user interface that allows users to interactively adjust constraint parameters in real-time. The constraint boundary is made dynamic rather than static, enabling continuous exploration of feasible and infeasible regions without manual reconfiguration steps.
Solution Approach 2:
The ADS system automatically updates the constraint boundary and re-evaluates design solutions when constraint parameters are modified through the GUI. The system self-adjusts by recomputing feasible/infeasible regions and updating the Pareto frontier without requiring manual intervention for each change.
2Reliability
If strict design constraints are applied to ensure feasibility, then solution reliability is improved, but the search space is limited and flexibility is reduced
Solution Approach 1:
The constraint boundary is rendered dynamic and adjustable through the graphical interface. Users can interactively modify constraint parameters to explore different regions of the design space, transitioning between strict feasibility (reliability-focused) and relaxed constraints (exploration-focused) modes as needed.
Solution Approach 2:
The system allows modification of constraint parameters through the GUI, enabling users to adjust constraint thresholds and boundary conditions. This parameter flexibility permits exploration of both feasible and infeasible regions while maintaining the ability to return to strict constraint adherence when reliability is prioritized.
3Manufacturing precision
If comprehensive constraint evaluation is performed to identify all feasible solutions, then solution quality is improved, but computational complexity increases
Solution Approach 1:
The design space is segmented into feasible and infeasible regions separated by a dynamically updated constraint boundary. The graphical interface displays these regions separately, allowing the system to focus computational effort on evaluating solutions in the feasible region while visually representing the infeasible region boundary without exhaustive evaluation of all possible designs.
Solution Approach 2:
The system provides visual feedback through the graphical interface showing the constraint boundary, feasible/infeasible regions, and Pareto frontier. This feedback mechanism allows users to assess solution quality and constraint satisfaction visually, reducing the need for exhaustive computational evaluation of every possible design parameter combination.
Data Source
AI summary
Methods for automated design search and corresponding systems and computer-readable mediums. A method (600) includes receiving (602) an automated design search (ADS) specification (154). The method includes receiving (604) design search data (166). The method includes receiving (606) a plurality of constraints (156), each constraint (156) having an associated constraint threshold (156) and computing and displaying (608) an initial output (400) of potential ADS solutions (158) according to the ADS specification (154), the design search data (166), and the constraints (156). The method includes receiving (610) a constraint relaxation input (162) from a user and, in response to receiving (610) the constraint relaxation input (162), computing and displaying (612) an updated output (500) of potential ADS solutions (158), according to the ADS specification (154), the constraints (156), the design search data (166), and the constraint relaxation input (162). The method can include dynamically and repeatedly updating (612) the updated output (500) when the constraint relaxation input (162) continues to be received (610).