Generative AI for Single-Iteration Aerospace System Design
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Solution Overview
Problem
Existing technologies struggle to provide rapid, holistic, and optimized assessment of complex systems such as aerospace vehicle systems, supply chain management, and military operations, due to limitations in handling nonconvex, discontinuous, and nondifferentiable optimization, lack of data infrastructure, and uncertainty in data, leading to inefficiencies and human subjectivity in design and control.
Innovation Solution
A generative artificial intelligence system that incorporates first-order physics-based engineering equations and expert system design rules, capable of handling stochastic variables and uncertain data, to autonomously generate optimal designs and assessments in a single iteration, reducing computational resources and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional iterative optimization methods are used for complex system design, then design accuracy can be improved, but computational time and processing power increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing design rules, constraints, and optimization criteria in a knowledge base before the actual design process. This allows the generative AI to rapidly retrieve and apply pre-validated design patterns without iterative computation, achieving both accuracy and speed.
Solution Approach 2:
The patent replaces traditional mechanical iterative optimization algorithms with a generative AI system that uses neural networks and knowledge-based reasoning. This substitution eliminates the need for repeated computational iterations while maintaining design quality through learned patterns and expert rules.
2Ease of manufacture
If traditional design methods are used, then existing tools can be maintained, but human subjectivity and inconsistency in design assessments persist
Solution Approach 1:
The system enables self-service by allowing the generative AI to autonomously perform design assessments and optimizations without human intervention. The AI independently evaluates design options against stored criteria and constraints, eliminating human subjectivity while maintaining consistency through objective, rule-based decision-making.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors design assessments against predefined objectives and constraints. This closed-loop approach ensures that design evaluations are consistently aligned with requirements, providing objective feedback that eliminates human bias and subjectivity.
3Reliability
If comprehensive system assessments are performed to capture complex interdependencies, then design quality improves, but computational resources and processing power increase
Solution Approach 1:
The system segments the complex design assessment into modular components, each handled by specialized AI models or knowledge bases. This segmentation allows the system to evaluate different aspects of design quality independently and efficiently, reducing overall computational energy while maintaining comprehensive assessment capability.
Solution Approach 2:
The patent dynamically adjusts assessment parameters and computational depth based on design complexity and stage. For early conceptual designs, the system uses simplified models with fewer parameters, while applying more detailed assessments only when necessary. This adaptive parameter adjustment maintains design quality while optimizing computational energy consumption.
Data Source
AI summary
A generative artificial intelligence system and method of operating the same to control complex systems. In one embodiment, the method includes incorporating measures of merit into system requirements for an aerospace vehicle system to provide aggregated system requirements and measures of merit, and receiving design parameters of the aerospace vehicle system represented as stochastic variables. The method also includes executing first order physics-based engineering equations of the design parameters with the generative artificial intelligence system on the processor to produce a design for an operation of the aerospace vehicle system to meet the aggregated system requirements and measures of merit in a single iteration improving computational efficiency and reducing power consumption of the processor operating the generative artificial intelligence system.