Flow Based

Flow-based methods are a class of machine learning techniques that model data distributions by transforming a simple distribution (e.g., Gaussian) into a complex target distribution through a series of invertible transformations. Current research focuses on applying these methods to diverse problems, including generative modeling, dynamical systems modeling, and signal processing, often employing neural networks, particularly normalizing flows and related architectures like GFlowNets. The ability of flow-based models to learn complex data distributions and perform efficient inference makes them valuable tools across various scientific disciplines and practical applications, such as image generation, network flow analysis, and physical system simulation.