Generative AI Design Using Physics Models for Complex Systems
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Solution Overview
Problem
Conventional systems struggle to holistically and optimally design complex systems due to limited user audience, lack of data infrastructure, information security concerns, and uncertainty in data, leading to inefficiencies and human subjectivity in design assessment and control.
Innovation Solution
A generative artificial intelligence system that incorporates measures of merit and first-order physics-based engineering equations to optimize design parameters, allowing for rapid, secure, and efficient generation of complex system designs and assessments, even in uncertain and non-convex environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional systems are used to design complex systems, then human expertise and judgment are applied, but computational efficiency is low and design assessment is subjective
Solution Approach 1:
The patent replaces human expert judgment and manual design assessment with an AI-based system that uses physics-based engineering equations and machine learning models. This substitution eliminates human subjectivity while maintaining design quality through objective, reproducible computational assessments that can evaluate complex systems holistically.
Solution Approach 2:
The patent introduces an AI-based intermediary system that acts as a bridge between design requirements and system optimization. This intermediary uses trained models and physics-based equations to objectively assess designs, eliminating the need for human experts to directly evaluate each design while preserving the essence of expert knowledge through pre-trained models.
2Manufacturing precision
If multiple iterations are performed for design optimization, then design quality improves, but power consumption and computational time increase
Solution Approach 1:
The patent performs preliminary actions by pre-training AI models on extensive datasets and pre-computing physics-based engineering equations during the model training phase. This allows the system to make rapid, accurate design assessments during actual use without requiring multiple iterative computations, significantly reducing real-time power consumption while maintaining high design optimization quality.
3Manufacturing precision
If comprehensive system assessment is performed, then design quality improves, but data infrastructure requirements and security risks increase
Solution Approach 1:
The patent extracts and separates critical assessment functions into standalone AI models that can be deployed independently. This extraction allows comprehensive system assessment to be performed through modular model invocations rather than requiring a monolithic complex infrastructure, reducing overall system complexity while maintaining holistic assessment capabilities.
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 a commercial operations system to provide aggregated system requirements and measures of merit, and receiving design parameters of the commercial operations 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 commercial operations 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.