@article{miles_cook_angers_swenson_kiedrowski_mattingly_smith_2021, title={Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations}, volume={207}, ISSN={["1943-7471"]}, DOI={10.1080/00295450.2020.1738796}, abstractNote={Abstract Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high-fidelity simulations of radiation transport within an urban domain. The model is constructed to employ a source location ( ) as input and return the estimated count rate for a set of specified detector locations. Because MCNP simulations are computationally expensive, we develop efficient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes and neural networks that we train and verify using the MCNP simulations. The trained surrogate models provide an efficient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source’s location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%.}, number={1}, journal={NUCLEAR TECHNOLOGY}, author={Miles, Paul R. and Cook, Jared A. and Angers, Zoey V. and Swenson, Christopher J. and Kiedrowski, Brian C. and Mattingly, John and Smith, Ralph C.}, year={2021}, month={Jan}, pages={37–53} }
 @article{cook_smith_hite_stefanescu_mattingly_2019, title={Application and Evaluation of Surrogate Models for Radiation Source Search}, volume={12}, ISSN={["1999-4893"]}, DOI={10.3390/a12120269}, abstractNote={Surrogate models are increasingly required for applications in which first-principles simulation models are prohibitively expensive to employ for uncertainty analysis, design, or control. They can also be used to approximate models whose discontinuous derivatives preclude the use of gradient-based optimization or data assimilation algorithms. We consider the problem of inferring the 2D location and intensity of a radiation source in an urban environment using a ray-tracing model based on Boltzmann transport theory. Whereas the code implementing this model is relatively efficient, extension to 3D Monte Carlo transport simulations precludes subsequent Bayesian inference to infer source locations, which typically requires thousands to millions of simulations. Additionally, the resulting likelihood exhibits discontinuous derivatives due to the presence of buildings. To address these issues, we discuss the construction of surrogate models for optimization, Bayesian inference, and uncertainty propagation. Specifically, we consider surrogate models based on Legendre polynomials, multivariate adaptive regression splines, radial basis functions, Gaussian processes, and neural networks. We detail strategies for computing training points and discuss the merits and deficits of each method.}, number={12}, journal={ALGORITHMS}, author={Cook, Jared A. and Smith, Ralph C. and Hite, Jason M. and Stefanescu, Razvan and Mattingly, John}, year={2019}, month={Dec} }