Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields

ArXi:2605.28625v1 Announce Type: new Generative modeling provides a powerful framework for learning data distributions. These models initially relied on probabilistic methods such as Gaussian Processes (GP) for uncertainty-aware predictions and shifted towards larger trainable models to learn complex distributions. In this work, we