  Heavy and light particles are commonly found in many natural phenomena and
industrial processes, such as suspensions of bubbles, dust, and droplets in
incompressible turbulent flows. Based on a recent machine learning approach
using a diffusion model that successfully generated single tracer trajectories
in three-dimensional turbulence and passed most statistical benchmarks across
time scales, we extend this model to include heavy and light particles. Given
the particle type - tracer, light, or heavy - the model can generate synthetic,
realistic trajectories with correct fat-tail distributions for acceleration,
anomalous power laws, and scale dependent local slope properties. This work
paves the way for future exploration of the use of diffusion models to produce
high-quality synthetic datasets for different flow configurations, potentially
allowing interpolation between different setups and adaptation to new
conditions.
