Cossan Software: A Multidisciplinary and Collaborative Software for Uncertainty Quantification

Patelli, Edoardo; Broggi, Matteo; Tolo, Silvia; Sadeghi, Jonathan

doi:10.7712/120217.5364.16982

Cited by 9 publications

(7 citation statements)

References 9 publications

(12 reference statements)

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“…This is a direct result from the truncation of the random variable that is associated to each predicted response. Also, since during the training of the IPM, the explicit constraint y i < y i < y i , i = 1, ..., n y is included, a similar observation can be made in this context, as demonstrated in [24] and [19]. Therefore, only the extreme bounds of the predicted response intervals need to be considered in the evaluation of the failure probability.…”

Section: Interval Predictor Modelmentioning

confidence: 89%

On the Robust Estimation of Small Failure Probabilities for Strong Nonlinear Models

Faes

Sadeghi

Broggi

et al. 2019

ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg

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Structural reliability methods are nowadays a cornerstone for the design of robustly performing structures, thanks to advancements in modeling and simulation tools. Monte Carlo-based simulation tools have been shown to provide the necessary accuracy and flexibility. While standard Monte Carlo estimation of the probability of failure is not hindered in its applicability by approximations or limiting assumptions, it becomes computationally unfeasible when small failure probability needs to be estimated, especially when the underlying numerical model evaluation is time consuming. In this case, variance reduction techniques are commonly employed, allowing for the estimation of small failure probabilities with a reduced number of samples and model calls. As a competing approach to variance reduction techniques, surrogate models can be used to substitute the computationally expensive model and performance function with an easy to evaluate numerical function calibrated through a supervised learning procedure. Both these tools provide accurate results for structural application. However, particular care should be taken into account when the reliability problems deal with high-dimensional or strongly nonlinear structural performances since the accuracy of the estimate is largely dependent on choices made during the surrogate modeling process. In this work, we compare the performance of the most recent state-of-the-art advance Monte Carlo techniques and surrogate models when applied to strongly nonlinear performance functions. This will provide the analysts with an insight to the issues that could arise in these challenging problems and help to decide with confidence on which tool to select in order to achieve accurate estimation of the failure probabilities within feasible times with their available computational capabilities.

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Section: Interval Predictor Modelmentioning

confidence: 89%

On the Robust Estimation of Small Failure Probabilities for Strong Nonlinear Models

Faes

Sadeghi

Broggi

et al. 2019

ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg

Self Cite

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“…[8]), the reader is instead referred to the position paper [9]. See also [2,10,11,12,13] for other recent contributions on IPMs, and [14,15] for a software and a fast algorithm.…”

Section: Discussion On Related Literaturementioning

confidence: 99%

On a class of interval predictor models with universal reliability

2019

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An Interval Predictor Model (IPM) is a rule by which some observable variables (system inputs) are mapped into an interval that is used to predict an inaccessible variable (system output). IPMs have been studied in [1], where the problem of fitting an IPM on a set of observations has been considered. In the same paper, upper-bounds on the probability that a future system output will fall outside the predicted interval (misprediction) have also been derived in a stationary and independent framework. While these bounds have the notable property of being valid independently of the unknown mechanism that has generated the data, in general the actual probability distribution of the misprediction does depend on the data generation mechanism and, hence, these bounds may introduce conservatism when applied to a specific case. In this paper, we study the reliability of an important class of IPMs, called minimax layers, and show that this class exhibits the special property that the probability distribution of the misprediction is known exactly and is universal, i.e., is always the same irrespective of the data generation mechanism. This result carries important consequences on the use of minimax layers in practice.

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“…The Monte Carlo simulation produced the closest results to the uncertainty process with many iterations (Stepanov and Amosov, 2007). Several studies used it to model GHI uncertainty, such as Alfi et al, 2017;Patelli et al, 2017;andYin andChen, 2017. Alfi et al, 2017 used the Monte Carlo simulation to model solar irradiance uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

The Prospect of Rooftop Photovoltaic Development Considering Global Horizontal Irradiation Uncertainty and Government Policies: A Case of Java Island, Indonesia

Budi

Sarjiya²,

Hadi³

2022

IJEEP

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Global horizontal irradiation uncertainty and government policy significantly affect the economics of rooftop photovoltaic development. Unfortunately, previous studies neglect these aspects. Therefore, it is necessary to undertake the economic analysis of rooftop photovoltaic development by considering its global horizontal irradiation uncertainty and government policies. This research creates a model that can be used to perform analysis and assess rooftop photovoltaic development prospects by considering irradiation uncertainty and government policies. The attractiveness of rooftop photovoltaic development is indicated by the value of the Levelized cost of electricity, net present value, and internal rate of return. Monte Carlo simulation models irradiation uncertainty. Java Island in Indonesia is used as a case study to show the performance of the proposed model. The research results show that the rooftop photovoltaic is not economically viable if built by the utility. Rooftop photovoltaic systems are only profitable when built by the private sector and is used for internal usage only. These rooftop photovoltaic investments provide an internal rate of return above 14%, which shows that investors with green capital funds and local investors are interested in investing. The emission reductions from a 100 m2 rooftop photovoltaic system vary from 214 to 384 tons of CO2 per year.

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Cossan Software: A Multidisciplinary and Collaborative Software for Uncertainty Quantification

Cited by 9 publications

References 9 publications

On the Robust Estimation of Small Failure Probabilities for Strong Nonlinear Models

On the Robust Estimation of Small Failure Probabilities for Strong Nonlinear Models

On a class of interval predictor models with universal reliability

The Prospect of Rooftop Photovoltaic Development Considering Global Horizontal Irradiation Uncertainty and Government Policies: A Case of Java Island, Indonesia

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