AI Antenna Element Assembly for Faster Performance Optimization
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Solution Overview
Problem
The design of antennas is often time-intensive and limited by the knowledge and proclivities of engineers, requiring significant effort to achieve specific gain, efficiency, and frequency band specifications, with existing methods being costly and time-consuming.
Innovation Solution
The implementation of artificial intelligence (AI) systems that access prior antenna designs and specifications to design, simulate, and optimize antennas by arranging antenna elements, using surrogate models for rapid performance prediction and optimization, and adjusting geometric parameters to meet design criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional antenna design methods are used, then design accuracy and performance optimization can be achieved, but the design process becomes time-intensive and costly
Solution Approach 1:
The patent creates a digital twin or virtual model of the antenna design process using machine learning algorithms. The system trains on existing antenna designs and their performance data to generate accurate predictions about new designs without requiring extensive physical prototyping and testing, thereby maintaining design accuracy while reducing time consumption
Solution Approach 2:
The system uses machine learning to rapidly evaluate multiple design parameters and their interactions. By automatically adjusting geometric parameters, material properties, and structural configurations based on learned patterns from training data, the system can optimize antenna performance much faster than traditional manual iteration while maintaining or improving design accuracy
2Reliability
If engineers manually design antennas with extensive iteration, then specific performance specifications can be met, but the process requires significant engineer effort and expertise
Solution Approach 1:
The system enables automated self-optimization of antenna designs through machine learning algorithms that automatically adjust design parameters to meet performance specifications. The AI model learns from existing designs and autonomously generates optimized configurations without requiring continuous human intervention, thereby maintaining reliability while significantly increasing design throughput
Solution Approach 2:
The system implements automated feedback loops where simulation results and performance measurements are fed back into the machine learning model. This allows the system to learn from successes and failures, continuously improving its ability to meet performance specifications while rapidly generating multiple design iterations that would be too time-consuming for manual evaluation
3Manufacturing precision
If comprehensive simulation and testing are performed on each design iteration, then design quality can be ensured, but computational cost and time increase significantly
Solution Approach 1:
The system employs a two-stage evaluation approach: first using a fast, approximate machine learning model to screen and filter design iterations, then performing comprehensive simulations only on the most promising candidates. This partial application of full simulation resources maintains design quality for final selections while dramatically reducing overall computational energy consumption by avoiding exhaustive testing of all iterations
Data Source
AI summary
The disclosed computer-implemented method may include accessing various antenna elements and identifying parameters for an antenna that is to be formed using the accessed antenna elements. The method may also include assembling the antenna elements, using an artificial intelligence (AI) instance, into an assembled antenna that at least partially complies with the identified parameters. Various other methods, systems, and computer-readable media are also disclosed.