NSE - Nuclear Science & Engineering at MIT


Benoit Forget

Benoit Forget

Korea Electric Power Professor of Nuclear Engineering
Department Head and Professor of Nuclear Science and Engineering

Nancy Iappini (Senior Admin Assistant) 617-253-8667

MIT Computational Reactor Physics Group
Consortium for Advanced Simulation of Light Water Reactors (CASL)



PhD, Georgia Institute of Technology, 2006
MS, École Polytechnique de Montréal, 2003
B.S., École Polytechnique de Montréal, 2003


  • Landis Young Member Engineering Achievement Award, American Nuclear Society, 2013
  • Ruth and Joel Spira Award for Distinguished Teaching
  • ANS Faculty/PIA Teaching Award


Prof. Forget’s research interests are in the areas of computational reactor physics, radiative transport, and high performance computing. He is also the founder of the Computational Reactor Physics Group (CRPG).

Monte Carlo transport methods

Monte Carlo neutron transport methods have long been considered a reference solution since they model explicitly the random walk of neutrons in a nuclear system using the fundamental nuclear data. Recent advances in computing power bring these methods closer to performing realistic core analysis and design. CRPG has developed an open source Monte Carlo code, OpenMC, to perform large scale reactor analysis on modern computing architecture. CRPG focuses on the development of novel algorithms for improving the parallel efficiency of Monte Carlo methods, new communication schemes to reduce network load for massive tallies, and improved nuclear models to reduce memory requirements and increase fidelity. Current work is also focused on the analysis of inter-cycle correlations and the automation of Monte Carlo to achieve a given target accuracy.

Deterministic transport methods

In addition to the Monte Carlo work, CRPG has also developed new concepts to attain high-fidelity 3D deterministic simulations. This work has led to the development of OpenMOC, a 3D method of characteristics code designed for efficient multi-threading and low memory requirement. This project seeks to reduce current bottlenecks limiting the applicability of such methods to full core analysis, and develop new acceleration schemes for more efficient analysis.

Multiphysics coupling

CRPG also focuses on the development of methods enabling coupling of reactor physics codes with fuel performance or thermal-hydraulics codes for both steady-state and transient analysis. Current work focuses on the coupling of OpenMC with external frameworks using novel techniques to transfer information to irregular meshes and use returning temperature distributions within the Monte Carlo simulations. This work also focuses on the development of new approaches for dealing with the temperature dependence of nuclear data as to provide accurate feedback.

Uncertainty Quantification

CRPG is also exploring the area of nuclear data uncertainties by looking at the sensitivity of resonance parameters and the propagation of uncertainties on a given nuclear system. This information will not only provide a measure of uncertainty for quantities of interest, but can also better inform nuclear data evaluators for future data releases.


Jiankai Yu, Benoit Forget, “Verification of depletion capability of OpenMC using VERA depletion benchmark”, Annals of Nuclear Energy, 170, 2022.

B. Forget, A. Alhajri, “Normalizing flows for thermal scattering sampling”, Annals of Nuclear Energy, 170, 2022.

S. Harper, K. Smith, B.Forget “Efficient MC–CMFD Multiphysics”, Annals of Nuclear Energy, 165, 2022.

P. Ducru, B. Forget, V. Sobes, G. Hale, and M. Paris, “Shadow poles in the alternative parametrization of R-matrix theory,” Phys. Rev. C, 103, 2021.

P. Ducru, B. Forget, V. Sobes, G. Hale, and M. Paris, “Scattering matrix pole expansions for complex wavenumbers in R-matrix theory” Phys. Rev. C, 103, 2021.

P. Ducru, A. Alhajri, I. Meyer, B. Forget, V.Sobes, C. Josey, and J. Liang “Windowed multipole representation of R-matrix cross sections,” Phys. Rev. C, 103, 2021.

M. I. Radaideh, I. Wolverton, J. Joseph, J. Tusar, U. Otgonbaatard, N. Roy, B. Forget, K. Shirvan, “Physics-informed reinforcement learning optimization of nuclear assembly design”, Nuclear Engineering and Design, 372, 2021.

S. Kumar, J. Liang, B. Forget, K. Smith, “BEAVRS: An integral full core multi-physics PWR benchmark with measurements and uncertainties”, Progress in Nuclear Energy, 129, 2020.

S. Kumar, B. Forget, K. Smith, “Stationarity Diagnostics using Functional Expansion Tallies”, Annals of Nuclear Energy, 143, 2020.

J.Miao, B.Forget, K.Smith, “Correlation diagnosis method for heterogeneous Monte Carlo eigenvalue simulations based on a diffusion approximation”, Annals of Nuclear Energy, 130, 301-318, 2019.

W.Boyd, B.Forget, K.Smith “A single-step framework to generate spatially self-shielded multi-group cross sections from Monte Carlo transport simulations,” Annals of Nuclear Energy, 125, 261-271, 2019.


B. Forget and F. Rahnema, "Boundary Adjusted Critical Spectrum Methodology for Reactor Lattice Depletion", US Patent 7676015 Issued on March 9th, 2010.


22.05 Neutron Science and Reactor Physics
22.13 Nuclear Energy Systems
22.211 Nuclear Reactor Physics I
22.212 Nuclear Reactor Physics II


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