Global sensitivity analysis in stochastic simulators of uncertain reaction networks

Jimenez, Maria Navarro; Maître, Olivier Le; Knio, Omar M.

doi:10.1063/1.4971797

Cited by 10 publications

(2 citation statements)

References 41 publications

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“…For these problems, direct CME solvers can be a time-saving alternate [6]. Direct computation of the solution to the CME also has applications in uncertainty quantification, sensitivity analysis, and experimental design [14,19,22,38].…”

Section: Discussionmentioning

confidence: 99%

An adaptive solution to the chemical master equation using quantized tensor trains with sliding windows

Dinh

Sidje

2020

Phys. Biol.

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To cope with an extremely large or even infinite state space when solving the chemical master equation in biological problems, a potent strategy is to restrict to a finite state projection (FSP) and represent the transition matrix and probability vector in quantized tensor train (QTT) format, leading to savings in storage while retaining accuracy. In an earlier adaptive FSP–QTT algorithm, the multidimensional state space was downsized and kept in the form of a hyper rectangle that was updated when needed by selectively doubling some of its side dimensions. However, this could result in a much larger state space than necessary, with the effect of hampering both the execution time and stepping scheme. In this work, we improve the algorithm by enabling sliding windows that can dynamically slide, shrink or expand, with updates driven by a number of stochastic simulation algorithm trajectories. The ensuing state space is a considerably reduced hyper rectangle containing only the most probable states at each time step. Three numerical experiments of varying difficulty are performed to compare our approach with the original adaptive FSP–QTT algorithm.

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Section: Discussionmentioning

confidence: 99%

An adaptive solution to the chemical master equation using quantized tensor trains with sliding windows

Dinh

Sidje

2020

Phys. Biol.

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“…It has in fact shown to be an effective tool in a different areas, including ocean modeling applications (Srinivasan et al, 2010;Alexanderian et al, 2012;Winokur et al, 2013;Goncalves et al, 2016;Wang et al, 2016) among others [e.g. (Navarro Jimenez et al, 2016;Huan et al, 2018;KC et al, 2021)].…”

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confidence: 99%

Variance-based sensitivity analysis of oil spill predictions in the Red Sea region

et al. 2023

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To support accidental spill rapid response efforts, oil spill simulations may generally need to account for uncertainties concerning the nature and properties of the spill, which compound those inherent in model parameterizations. A full detailed account of these sources of uncertainty would however require prohibitive resources needed to sample a large dimensional space. In this work, a variance-based sensitivity analysis is conducted to explore the possibility of restricting a priori the set of uncertain parameters, at least in the context of realistic simulations of oil spills in the Red Sea region spanning a two-week period following the oil release. The evolution of the spill is described using the simulation capabilities of Modelo Hidrodinâmico, driven by high-resolution metocean fields of the Red Sea (RS) was adopted to simulate accidental oil spills in the RS. Eight spill scenarios are considered in the analysis, which are carefully selected to account for the diversity of metocean conditions in the region. Polynomial chaos expansions are employed to propagate parametric uncertainties and efficiently estimate variance-based sensitivities. Attention is focused on integral quantities characterizing the transport, deformation, evaporation and dispersion of the spill. The analysis indicates that variability in these quantities may be suitably captured by restricting the set of uncertain inputs parameters, namely the wind coefficient, interfacial tension, API gravity, and viscosity. Thus, forecast variability and confidence intervals may be reasonably estimated in the corresponding four-dimensional input space.

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Sensitivity-Driven Adaptive Construction of Reduced-space Surrogates

et al. 2018

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We develop a systematic approach for surrogate model construction in reduced input parameter spaces. A sparse set of model evaluations in the original input space is used to approximate derivative based global sensitivity measures (DGSMs) for individual uncertain inputs of the model. An iterative screening procedure is developed that exploits DGSM estimates in order to identify the unimportant inputs. The screening procedure forms an integral part of an overall framework for adaptive construction of a surrogate in the reduced space. The framework is tested for computational efficiency through an initial implementation in simple test cases such as the classic Borehole function, and a semilinear elliptic PDE with a random source term. The framework is then deployed for a realistic application from chemical kinetics, where we study the ignition delay in an H 2 /O 2 reaction mechanism with 19 uncertain rate constants. It is observed that significant computational gains can be attained by constructing accurate low-dimensional surrogates using the proposed framework.

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Global sensitivity analysis in stochastic simulators of uncertain reaction networks

Cited by 10 publications

References 41 publications

An adaptive solution to the chemical master equation using quantized tensor trains with sliding windows

An adaptive solution to the chemical master equation using quantized tensor trains with sliding windows

Variance-based sensitivity analysis of oil spill predictions in the Red Sea region

Sensitivity-Driven Adaptive Construction of Reduced-space Surrogates

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