Advancing Rocket Launch Simulations with Hyperdimensional Computing: A Deep Dive into Neuro-Symbolic Reasoning and Hypervector Encoding

Introduction

In this article, we will walk through my newly improved rocket launch simulation that now leverages hyperdimensional computing (HDC) for more efficient and robust classification tasks. This approach replaces the earlier version of the simulation, which used a convolutional neural network (CNN) architecture for classifying rocket launch altitudes based on simulated trajectories. While the old approach had its strengths, the new method integrates neuro-symbolic reasoning and hypervector encoding, offering a unique solution for complex classification tasks.

The initial approach (version 1) involved generating rocket launch trajectories, transforming the results into images, and then passing those images through a CNN for classification. The process was computationally intensive due to the high-dimensional image data and the deep layers required for CNNs to make accurate predictions. Although CNNs are powerful, they come with certain drawbacks: computational complexity, overfitting risks, and substantial training data requirements. As such, CNN-based architectures are not always the most efficient choice for real-time systems, especially in scenarios requiring high…