Ray Path Computation Using Neural Network Geodesic and Raytracing
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Solution Overview
Problem
Existing computational electromagnetics methods, such as Geometric Theory of Diffraction (GTD) and Uniform Theory of Diffraction (UTD), struggle to accurately determine ray paths on complex geometries, leading to computationally complex and costly simulations, especially for large and complex structures.
Innovation Solution
A computing system utilizing a trained neural network, such as a graph neural network (GNN) or a greedy search algorithm, combined with heat vector field computation and raytracing, to efficiently calculate geodesic and free space paths between antenna locations, overcoming the limitations of traditional backtracing and Eikonal equation methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional backtracing and Eikonal equation methods are used to compute ray paths, then measurement precision of ray paths is maintained, but device complexity and computational cost increase significantly
Solution Approach 1:
The patent pre-computes and stores geodesic paths between all pairs of mesh vertices using the Heat Method before actual ray tracing. This preliminary computation creates a lookup table that stores pre-calculated geodesic information, allowing the system to quickly retrieve and combine these pre-computed paths with free-space ray segments without performing complex real-time calculations during simulation.
Solution Approach 2:
The patent replaces traditional mechanical/geometric backtracing methods with a heat diffusion-based mathematical approach. The Heat Method uses partial differential equations to compute geodesic paths by simulating heat flow on the mesh surface, substituting the iterative geometric backtracing process with a continuous field-based computation that is more efficient for complex geometries.
2Reliability
If traditional ray path computation methods are used, then reliability of electromagnetic simulation is maintained, but productivity and simulation speed decrease
Solution Approach 1:
The system performs geodesic path computation in advance and stores results in a lookup table. During electromagnetic simulation, the system retrieves pre-computed geodesic paths and combines them with free-space ray segments, eliminating the need for repeated complex calculations and significantly accelerating simulation speed while maintaining accuracy.
Solution Approach 2:
The patent divides the ray path computation into two distinct segments: geodesic paths along the mesh surface and free-space paths between mesh vertices. This segmentation allows each segment to be computed using optimized methods appropriate to its characteristics, with geodesic segments retrieved from pre-computed data and free-space segments calculated using standard ray tracing techniques.
3Measurement precision
If complex geometries are analyzed using traditional methods, then measurement precision of diffraction properties is improved, but loss of time in computation increases
Solution Approach 1:
The patent pre-computes geodesic paths for all vertex pairs on the mesh before performing diffraction analysis. This preliminary computation stores the complex geometric path information in advance, allowing the system to quickly retrieve and use these paths during electromagnetic simulation without repeating time-consuming calculations, thus reducing computation time while maintaining precision for complex geometries.
Data Source
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AI summary
A computing system (10) including a processor (14) configured to receive a mesh (30) of a three-dimensional geometry (38). The processor is further configured to receive a source antenna location (40) and a destination antenna location (42) on the mesh. The processor is further configured to compute a ray path (60) as an estimated shortest path between the source antenna location and the destination antenna location. The ray path includes a geodesic path (62) over the mesh and a free space path (64) outside the mesh. The ray path is computed at least in part by computing the geodesic path at least in part by performing inferencing at a trained neural network (52). Computing the ray path further includes computing the free space path at least in part by performing raytracing from a launch point (66) located at an endpoint of the geodesic path. The processor is further configured to output the ray path to an additional computing process (70).