We are developing a novel open-source framework for real-time high-fidelity neutron kinetics based on the hybrid transient fission matrix (TFM) methodology. 

Hybrid particle transport enables the solution of the 3-D time-dependent neutron transport equation with Monte Carlo-level accuracy while reducing the computation time by 3 or more orders of magnitude.

Artificial intelligence (AI) and machine learning (ML) applied to the TFM method can enable first-of-a-kind high-fidelity digital twins based on full 3-D time-dependent neutron transport, instead of lower-fidelity approaches (e.g., point kinetics and nodal diffusion). 

ML/AI algorithms can be used for optimizing the generation of high-fidelity response functions, learning dependencies of the TFM coefficients, and accelerating the time-convolution of the fission source.

The placement of detectors and instrumentation within the core of small and micro modular reactor is a challenge given the very limited real estate within the core. Scientists at Argonne National Laboratory have developed a methodology using on a machine-learned Green's function based on the neutron diffusion equation to achieve detailed in-core flux reconstruction using out-of-core detectors only. We are working with Argonne to validate their algorithm and evaluate the validity of the diffusion approximation.