Physics-Based Generative AI for Single-Iteration Threat Assessment
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Solution Overview
Problem
Current technologies are inadequate in rapidly and holistically assessing complex systems, such as aerospace vehicle design and threat analysis, due to limitations in handling nonconvex, discontinuous, and uncertain data, which are common in systems like supply chain management and military operations.
Innovation Solution
A generative artificial intelligence system that executes first-order physics-based engineering equations to autonomously generate designs and assessments, incorporating stochastic variables and expert system design rules, allowing for rapid, secure, and optimized control of complex systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional assessment methods are used for complex systems, then human expertise and judgment are applied, but the process is slow and lacks computational efficiency
Solution Approach 1:
The patent replaces traditional mechanical human assessment processes with an AI-based computational system that executes physics-based engineering equations. This substitution enables rapid, automated assessment of complex systems while maintaining analytical rigor through first-order physics equations, thereby improving productivity and reducing assessment time.
Solution Approach 2:
The AI system autonomously performs assessments by executing physics-based equations and generating design evaluations without requiring continuous human intervention. The system serves itself by automatically processing input data, running simulations, and producing comprehensive system assessments, thereby accelerating the assessment process.
2Productivity
If conventional design assessment approaches are used, then iterative processes are required, but computational efficiency and power consumption are reduced
Solution Approach 1:
The patent implements preliminary action by pre-defining physics-based engineering equations and design rules that guide the AI system's assessment process. These pre-established mathematical models and constraints enable the system to efficiently evaluate designs without requiring extensive iterative computations, thereby improving computational efficiency and reducing energy consumption.
Solution Approach 2:
The system changes parameters by transitioning from traditional iterative design optimization to a direct physics-based evaluation approach. By using first-order physics equations with predefined parameters and constraints, the AI system can assess designs more efficiently, reducing the computational iterations required and thereby lowering processor power consumption.
3Reliability
If traditional design methods are used, then human subjectivity influences design decisions, but consistency and objectivity are reduced
Solution Approach 1:
The patent implements feedback mechanisms through physics-based equations that provide objective, consistent evaluation criteria for design assessments. The AI system uses these equations to automatically evaluate designs against predefined performance metrics and constraints, eliminating human subjectivity and ensuring consistent, reliable assessments across different design scenarios.
Solution Approach 2:
The system achieves universality by creating a standardized AI-based assessment framework that can evaluate various complex systems (aerospace vehicles, supply chains, military operations) using the same physics-based engineering equations. This multi-functional approach ensures consistent, objective assessments across different domains while managing system complexity through reusable mathematical models.
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 receiving performance metrics for a threat system represented as stochastic variables. The method also includes executing first order physics-based engineering equations of the performance metrics with the generative artificial intelligence system on the processor to produce a threat analysis of the threat system to meet the performance metrics in a single iteration improving computational efficiency and reducing power consumption of the processor operating the generative artificial intelligence system.