Generative AI Design Assessment With Physics-Based Single-Pass Inference
Find Innovative SolutionsGenerate Solutions
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 incorporates first-order physics-based engineering equations and expert system design rules to autonomously generate designs and assessments, capable of handling stochastic variables and optimizing system performance in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional iterative design methods are used to assess complex systems, then design accuracy can be improved through multiple iterations, but computational time and power consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing input data and pre-computing relevant parameters before the main design assessment. This includes standardizing input formats, pre-calculating baseline metrics, and preparing reference data structures that enable the neural network to operate more efficiently during actual design assessments, thereby reducing iterative computational time while maintaining accuracy
Solution Approach 2:
The patent replaces traditional mechanical iterative computation systems with a neural network-based computational model. The neural network learns optimal design assessments from training data and can perform evaluations in a single pass without requiring multiple iterative cycles, fundamentally changing the computational mechanism from iterative refinement to direct inference
2Reliability
If comprehensive system assessments are performed to handle nonconvex, discontinuous, and uncertain data, then assessment quality improves, but computational resource requirements increase
Solution Approach 1:
The system changes parameters by transforming complex, nonconvex, discontinuous data into standardized continuous representations that the neural network can process efficiently. This includes converting uncertain data into probability distributions, transforming discontinuous system states into continuous feature vectors, and normalizing diverse input parameters into unified scales, thereby maintaining assessment quality while reducing computational energy requirements
3Adaptability or versatility
If human experts conduct manual assessments of complex systems, then subjective judgment can be applied, but time consumption and human resource requirements increase
Solution Approach 1:
The system implements self-service by enabling the neural network to autonomously perform design assessments without requiring human expert intervention for each evaluation. The network learns from training data and independently handles data processing, analysis, and assessment generation, thereby maintaining expert-level adaptability while dramatically increasing productivity and eliminating manual time 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 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.