The field of outdoor tracking and aerodynamic optimization is rapidly advancing, with a focus on developing innovative solutions that integrate cutting-edge technologies such as Real-Time Kinematic positioning, Optical See-Through Head-Mounted Displays, and deep reinforcement learning. Researchers are exploring new approaches to optimize aerodynamic performance, including the use of neural networks, generative 3D shape optimization, and triplane-based implicit neural representations. These advancements have the potential to significantly improve the efficiency and accuracy of outdoor tracking and aerodynamic optimization, with applications in various fields such as aviation, transportation, and construction. Noteworthy papers include:

• A study on the Intrinsic Dimension Estimating Autoencoder, which introduces a novel approach to estimating the intrinsic dimension of datasets and demonstrates its effectiveness in reconstructing original datasets.
• The TripOptimizer framework, which employs a Variational Autoencoder to optimize 3D shapes for reduced drag coefficients, achieving drag coefficient reductions of up to 11.8%.
• A paper on Discovering Flow Separation Control Strategies, which applies deep reinforcement learning to active flow control and discovers control strategies that enhance lift and reduce drag.