About

Particle and nuclear physics (PNP) are fundamentally probabilistic due to quantum mechanics, and rely on complex Monte-Carlo (MC)-based simulators to make stochastic predictions for nearly all aspects of experimental design and data interpretation. In fact, most branches of science and engineering rely heavily on MC simulations for solving difficult problems, from modeling traffic flow to predicting weather patterns; in the rapidly emerging fields of machine learning and quantum computing, MC methods are essential. Progress in these areas requires developing, validating, and deploying novel and efficient MC algorithms. However, many algorithm curricula focus on deterministic methods, with MC techniques covered only in passing, leading to a gap between knowledge and required skills for junior researchers. The goal of MCgen program is to fill this knowledge gap by training graduate students and junior postdoctoral researchers in the development of MC models with traineeships and schools focused on real-world PNP problems.

We were inspired in part by the highly successful MCnet in Europe.

Funding provided by NSF, grant OAC-2417682.