Méthode de Monte-Carlo Symbolique pour la caractérisation des propriétés thermophysiques : cas de la méthode flash

Path integrals formulations leading to propagator evaluation for coupled linear physics in large geometric models

“…the derivative with respect to each parameter, then the general theory of sensitivity evaluation in Monte Carlo algorithms could be used [23], but we would not build the complete dependence (the function) as we did here with the sources. Addressing the complete non-linear dependence on other parameters than sources is not theoretically unfeasible: it has notably been achieved in the field of radiative transfer under the literature name of "Symbolic Monte Carlo" [24,25,26,27] and we have started to work on extending these symbolic techniques to coupled heat transfer, with the objective of implementing them inside stardis-solver [28,29]. By far more difficult would be the question of addressing the dependence on geometrical parameters.…”

A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds Over Reflecting Surfaces

“…It was our intention to emphasize this aspect rather than the perhaps more obvious benefits of the FMC methods: the fact that they greatly accelerate MC methods, as only one simulation is needed to recover the amount of information that would normally be output from an arbitrary large number of standard simulations. This property has been useful in particular in the context of parameter identification or inversion (Dunn, 1981;Floyd et al, 1986;Maanane et al, 2020;Sans, Blanco, et al, 2021) and more generally has a great potential for global nonlinear parametric sensitivity analyses. In other words, FMC methods can be thought of as a physically-informed machine learning technique.…”