Diffusion Term

Diffusion terms, describing the spread or dispersal of quantities like probability or energy, are central to numerous scientific models and machine learning algorithms. Current research focuses on improving the efficiency and accuracy of learning these terms from data, employing techniques like optimal transport and novel neural network architectures (e.g., encoder-decoder CNNs, JKOnet*) to represent and solve the underlying diffusion equations. These advancements are impacting diverse fields, from generative modeling of images and other data to solving partial differential equations in physics and engineering, offering faster and more accurate solutions to complex problems. A key challenge remains understanding and mitigating biases inherent in learned diffusion models, particularly when applied to complex domains like food imagery.