N Chandrika , Venkata Ramana Guntreddi 1 1 ( October - December ) The use of realworld motorsport telemetry data is limited by its relative rarity commercial confidentiality and high cost not to mention a lack of quantity when needed for comprehensive training of aerostability prediction systems A Denoising Diffusion Probabilistic Model DDPM framework which learns a joint statistical and temporal relationship between multichannel vehicle sensor data and produces high fidelity training sequences indistinguishable from real telemetry from a race It features a UNet denoising backbone with cross channel temporal attention a linear noise schedule over T 1000 timesteps and a physicsinformed Stability Score consistency loss that allows to connect the generative process to the physics of aerodynamics The results of extensive evaluation experiments on simulated Formulalike telemetry channels covering channels speed acceleration suspension movement wings load tyre temperature and aerodynamic pressure show that the proposed diffusion model outperforms GAN VAE and LSTM synthesis baseline models on all three metrics 96 downstream classifier accuracy realism score of 094 and mean squared error between real and synthesized channels of 003 The framework is intended to be an underlying basis for aerostability artificial intelligence training pipelines that need to work on scarce data Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026