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Parameter identifiability and input–output equations [PDF]
Applicable Algebra in Engineering, Communication and Computing, 2021 Structural parameter identifiability is a property of a differential model with parameters that allows for the parameters to be determined from the model equations in the absence of noise. One of the standard approaches to assessing this problem is via input-output equations and, in particular, characteristic sets of differential ideals.
Ovchinnikov, Alexey +2 more
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Discovering an active subspace in a single-diode solar cell model [PDF]
, 2015 Predictions from science and engineering models depend on the values of the model's input parameters. As the number of parameters increases, algorithmic parameter studies like optimization or uncertainty quantification require many more model evaluations.
Campanelli, Mark +2 more
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Variance in System Dynamics and Agent Based Modelling Using the SIR Model of Infectious Disease [PDF]
, 2012 Classical deterministic simulations of epidemiological processes, such as those based on System Dynamics, produce a single result based on a fixed set of input parameters with no variance between simulations. Input parameters are subsequently modified on
Ahmed, Aslam +2 more
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Homogenization with uncertain input parameters [PDF]
Mathematica Bohemica, 2010 Summary: We homogenize a class of nonlinear differential equations set in highly heterogeneous media. Contrary to the usual approach, the coefficients in the equation characterizing the material properties are supposed to be uncertain functions from a given set of admissible data. The problem with uncertainties is treated by means of the worst scenario
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Input-output theory of the unconventional photon blockade [PDF]
, 2013 We study the unconventional photon blockade, recently proposed for a coupled-cavity system, in presence of input and output quantum fields. Mixing of the input or output channels still allows strong photon antibunching of the output field, but for ...
Flayac, H., Savona, V.
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Accounting for input-model and input-parameter uncertainties in simulation [PDF]
IIE Transactions, 2004 To account for the input-model and input-parameter uncertainties inherent in many simulations as well as the usual stochastic uncertainty, we present a Bayesian input-modeling technique that yields improved point and confidence-interval estimators for a selected posterior mean response. Exploiting prior information to specify the prior probabilities of
FAKER ZOUAOUI, JAMES R. WILSON
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Control vector parameterization with sensitivity based refinement applied to baking optimization [PDF]
, 2007 In bakery production, product quality attributes as crispness, brownness, crumb and water content are developed by the transformations that occur during baking and which are initiated by heating. A quality driven procedure requires process optimization
Boom, R.M. +4 more
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Optimal Input Design for Reduction of Parameter Correlations [PDF]
2018 24th International Conference on Automation and Computing (ICAC), 2018 An new scalarisation criterion is proposed for optimal experiment design (OED) of input intensity so as to obtain the most informative experimental data for parameter estimation with reduced parameter correlations. This criterion is a linear combination of logarithm function of the A-optimality and the modified E (ME)-optimality.
Ke Wang, Hong Yue, Hui Yu
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How Finely Tuned is Supersymmetric Dark Matter? [PDF]
, 2001 We introduce a quantification of the question in the title: the logarithmic sensitivity of the relic neutralino density Omega-hsquared to variations in input parameters such as the supersymmetric mass scales m_0, m_1/2 and A_0, tan beta and the top and ...
't Hooft +52 more
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