👉 Training Light Particles involves teaching a model to generate and manipulate light particles, which are fundamental units of light or photons in a computational context. This process typically starts with a dataset of light particles, where each particle is described by attributes such as intensity, color, position, and direction. The model is trained to understand the statistical properties and interactions of these particles, enabling it to predict new, synthetic light particles that adhere to the learned patterns. During training, the model uses techniques like generative adversarial networks (GANs) or variational autoencoders (VAEs) to learn the underlying distribution of light particles, allowing it to generate realistic and diverse light fields. This capability is useful in applications such as computer graphics, augmented reality, and scientific simulations, where realistic lighting and particle dynamics are crucial.
