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IEEE/ACM Transactions on Computational Biology and Bioinformatics
Sargsyan, Khachik S. ; Safta, Cosmin S. ; Debusschere, Bert D. ; Najm, H.N.
In this work, the problem of representing a stochastic forward model output with respect to a large number of input parameters is considered. The methodology is applied to a stochastic reaction network of competence dynamics in Bacillus subtilis bacterium. In particular, the dependence of the competence state on rate constants of underlying reactions is investigated. We base our methodology on Polynomial Chaos (PC) spectral expansions that allow effective propagation of input parameter uncertainties to outputs of interest. Given a number of forward model training runs at sampled input parameter values, the PC modes are estimated using a Bayesian framework. As an outcome, these PC modes are described with posterior probability distributions. The resulting expansion can be regarded as an uncertain response function and can further be used as a computationally inexpensive surrogate instead of the original reaction model for subsequent analyses such as calibration or optimization studies. Furthermore, the methodology is enhanced with a classification-based mixture PC formulation that overcomes the difficulties associated with representing potentially nonsmooth input-output relationships. Finally, the global sensitivity analysis based on the multiparameter spectral representation of an observable of interest provides biological insight and reveals the most important reactions and their couplings for the competence dynamics. © 2013 IEEE.
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Sargsyan, Khachik S. ; Safta, Cosmin S. ; Debusschere, Bert D. ; Najm, H.N.
In this work, the problem of representing a stochastic forward model output with respect to a large number of input parameters is considered. The methodology is applied to a stochastic reaction network of competence dynamics in Bacillus subtilis bacterium. In particular, the dependence of the competence state on rate constants of underlying reactions is investigated. We base our methodology on Polynomial Chaos (PC) spectral expansions that allow effective propagation of input parameter uncertainties to outputs of interest. Given a number of forward model training runs at sampled input parameter values, the PC modes are estimated using a Bayesian framework. As an outcome, these PC modes are described with posterior probability distributions. The resulting expansion can be regarded as an uncertain response function and can further be used as a computationally inexpensive surrogate instead of the original reaction model for subsequent analyses such as calibration or optimization studies. Furthermore, the methodology is enhanced with a classification-based mixture PC formulation that overcomes the difficulties associated with representing potentially nonsmooth input-output relationships. Finally, the global sensitivity analysis based on the multiparameter spectral representation of an observable of interest provides biological insight and reveals the most important reactions and their couplings for the competence dynamics. © 2013 IEEE.
Proposed for publication in International Journal for Uncertainty Quantification.
Safta, Cosmin S. ; Najm, H.N. ; Debusschere, Bert D.
Najm, H.N.
Ray, Jaideep R. ; Najm, H.N.
Najm, H.N.
Najm, H.N.
Sargsyan, Khachik S. ; Safta, Cosmin S. ; Debusschere, Bert D. ; Najm, H.N.
Multiscale Modeling and Simulation
Salloum, Maher S. ; Sargsyan, Khachik S. ; Jones, Reese E. ; Debusschere, Bert D. ; Najm, H.N. ; Adalsteinsson, Helgi A.
We present a methodology to assess the predictive fidelity of multiscale simulations by incorporating uncertainty in the information exchanged between the atomistic and continuum simulation components. Focusing on uncertainty due to finite sampling in molecular dynamics (MD) simulations, we present an iterative stochastic coupling algorithm that relies on Bayesian inference to build polynomial chaos expansions for the variables exchanged across the atomistic-continuum interface. We consider a simple Couette flow model where velocities are exchanged between the atomistic and continuum components. To alleviate the burden of running expensive MD simulations at every iteration, a surrogate model is constructed from which samples can be efficiently drawn as data for the Bayesian inference. Results show convergence of the coupling algorithm at a reasonable number of iterations. The uncertainty associated with the exchanged variables significantly depends on the amount of data sampled from the MD simulations and on the width of the time averaging window used in the MD simulations. Sequential Bayesian updating is also implemented in order to enhance the accuracy of the stochastic algorithm predictions. © 2012 Society for Industrial and Applied Mathematics.
Najm, H.N. ; Debusschere, Bert D. ; Eldred, Michael S.
Najm, H.N. ; Zador, Judit Z.
Najm, H.N. ; Zador, Judit Z.
Optimization of new transportation fuels and engine technologies requires the characterization of the combustion chemistry of a wide range of fuel classes. Theoretical studies of elementary reactions — the building blocks of complex reaction mechanisms — are essential to accurately predict important combustion processes such as autoignition of biofuels. The current bottleneck for these calculations is a user-intensive exploration of the underlying potential energy surface (PES), which relies on the “chemical intuition” of the scientist to propose initial guesses for the relevant chemical configurations. For newly emerging fuels, this approach cripples the rate of progress because of the system size and complexity. The KinBot program package aims to accelerate the detailed chemical kinetic description of combustion, and enables large-scale systematic studies on the sub-mechanism level.
Liu, Zhen L. ; Safta, Cosmin S. ; Sargsyan, Khachik S. ; van Bloemen Waanders, Bart G. ; Debusschere, Bert D. ; Najm, H.N. ; Bambha, Ray B.
Debusschere, Bert D. ; Najm, H.N. ; Safta, Cosmin S. ; Sargsyan, Khachik S.
Najm, H.N. ; Sargsyan, Khachik S. ; Safta, Cosmin S. ; Debusschere, Bert D. ; Jakeman, John D. ; Eldred, Michael S.
Najm, H.N.
Najm, H.N. ; Safta, Cosmin S. ; Debusschere, Bert D. ; Sargsyan, Khachik S.
Proposed for publication in International Journal for Uncertainty Quantification.
Najm, H.N. ; Safta, Cosmin S. ; Sargsyan, Khachik S. ; Debusschere, Bert D.
Najm, H.N. ; Debusschere, Bert D. ; Safta, Cosmin S. ; Sargsyan, Khachik S.
Najm, H.N.
Najm, H.N.
Najm, H.N.
Najm, H.N.
Sargsyan, Khachik S. ; Safta, Cosmin S. ; Debusschere, Bert D. ; Najm, H.N.
Safta, Cosmin S. ; Sargsyan, Khachik S. ; Debusschere, Bert D. ; Najm, H.N.
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