\Nu^2$ Flow

$\nu^2$-Flows and related normalizing flow methods represent a powerful new approach to reconstructing neutrino momenta in high-energy physics experiments, improving accuracy and speed compared to traditional techniques. Current research focuses on extending these methods to handle multiple neutrinos and incorporating them into probabilistic forecasting models for irregular time series data, addressing challenges like marginalization consistency. These advancements offer significant improvements in data analysis precision for particle physics and enhance the capabilities of probabilistic forecasting models across various scientific domains. The resulting gains in accuracy and efficiency have substantial implications for data analysis and model development in these fields.