RMC is a Python library for sampling from high-dimensional distributions via Monte Carlo-like algorithms as well as advanced AI/ML-based generative models.
RMC is part of the FY25 LDRD-ER project "Generative Modeling for Sampling" (20250391ER)
RMC has been approved for public release by the Feynman Innovation Center (O#=O4918)
© 2025. Triad National Security, LLC. All rights reserved. This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S. Department of Energy/National Nuclear Security Administration. All rights in the program are reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear Security Administration. The Government is granted for itself and others acting on its behalf a nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare. derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.
To build code documentation use the sphinx package. Under docs folder execute
make clean
make html
Authors/developers: Cristina Garcia-Cardona (@crstngc), Pratik Khandagale(@pkhandag), Yen Ting Lin (@dblueeye)
Affiliation: Information Sciences Group (CCS/CAI-3), Los Alamos National Laboratory