The Experts below are selected from a list of 11205 Experts worldwide ranked by ideXlab platform
Kevin Burrage - One of the best experts on this subject based on the ideXlab platform.
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estimates of the coverage of parameter space by latin hypercube and orthogonal array based Sampling
Applied Mathematical Modelling, 2017Co-Authors: Diane Donovan, Kevin Burrage, Pamela Burrage, T A Mccourt, Bevan Thompson, Emine şule YaziciAbstract:Abstract In this paper we use counting arguments to prove that the expected percentage coverage of a d dimensional parameter space of size n when performing k trials with either Latin Hypercube Sampling or Orthogonal Array-based Latin Hypercube Sampling is the same. We then extend these results to an experimental design setting by projecting onto a t
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populations of models experimental designs and coverage of parameter space by latin hypercube and orthogonal Sampling
arXiv: Methodology, 2015Co-Authors: Diane Donovan, Kevin Burrage, Pamela Burrage, Bevan ThompsonAbstract:In this paper we have used simulations to make a conjecture about the coverage of a $t$ dimensional subspace of a $d$ dimensional parameter space of size $n$ when performing $k$ trials of Latin Hypercube Sampling. This takes the form $P(k,n,d,t)=1-e^{-k/n^{t-1}}$. We suggest that this coverage formula is independent of $d$ and this allows us to make connections between building Populations of Models and Experimental Designs. We also show that Orthogonal Sampling is superior to Latin Hypercube Sampling in terms of allowing a more uniform coverage of the $t$ dimensional subspace at the sub-block size level.
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populations of models experimental designs and coverage of parameter space by latin hypercube and orthogonal Sampling
International Conference on Conceptual Structures, 2015Co-Authors: Diane Donovan, Kevin Burrage, Pamela Burrage, Bevan ThompsonAbstract:In this paper we have used simulations to make a conjecture about the coverage of a t-dimensional subspace of a d-dimensional parameter space of size n when performing k trials of Latin Hypercube Sampling. This takes the form P(k,n,d,t) = 1 - e^(-k/n^(t-1)). We suggest that this coverage formula is independent of d and this allows us to make connections between building Populations of Models and Experimental Designs. We also show that Orthogonal Sampling is superior to Latin Hypercube Sampling in terms of allowing a more uniform coverage of the t-dimensional subspace at the sub-block size level. These ideas have particular relevance when attempting to perform uncertainty quantification and sensitivity analyses.
Shijie Cheng - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Load Flow Method Based on Nataf Transformation and Latin Hypercube Sampling
IEEE Transactions on Sustainable Energy, 2013Co-Authors: Yan Chen, Jinyu Wen, Shijie ChengAbstract:This paper proposed a probabilistic load flow method that can address the correlated power sources and loads. The proposed probabilistic load flow method is based on the Nataf transformation and the Latin Hypercube Sampling. The main advantage of the proposed method is that high accurate solution can be obtained with less computation. Also, it is almost unconstrained for the probability distributions of the input random variables. Considering the uncertainties of correlated wind power, solar energy and loads, the effectiveness and the accuracy of the proposed probabilistic load flow method are verified by the comparative tests in a modified IEEE 14-bus system and a modified IEEE 118-bus system.
Bevan Thompson - One of the best experts on this subject based on the ideXlab platform.
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estimates of the coverage of parameter space by latin hypercube and orthogonal array based Sampling
Applied Mathematical Modelling, 2017Co-Authors: Diane Donovan, Kevin Burrage, Pamela Burrage, T A Mccourt, Bevan Thompson, Emine şule YaziciAbstract:Abstract In this paper we use counting arguments to prove that the expected percentage coverage of a d dimensional parameter space of size n when performing k trials with either Latin Hypercube Sampling or Orthogonal Array-based Latin Hypercube Sampling is the same. We then extend these results to an experimental design setting by projecting onto a t
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populations of models experimental designs and coverage of parameter space by latin hypercube and orthogonal Sampling
arXiv: Methodology, 2015Co-Authors: Diane Donovan, Kevin Burrage, Pamela Burrage, Bevan ThompsonAbstract:In this paper we have used simulations to make a conjecture about the coverage of a $t$ dimensional subspace of a $d$ dimensional parameter space of size $n$ when performing $k$ trials of Latin Hypercube Sampling. This takes the form $P(k,n,d,t)=1-e^{-k/n^{t-1}}$. We suggest that this coverage formula is independent of $d$ and this allows us to make connections between building Populations of Models and Experimental Designs. We also show that Orthogonal Sampling is superior to Latin Hypercube Sampling in terms of allowing a more uniform coverage of the $t$ dimensional subspace at the sub-block size level.
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populations of models experimental designs and coverage of parameter space by latin hypercube and orthogonal Sampling
International Conference on Conceptual Structures, 2015Co-Authors: Diane Donovan, Kevin Burrage, Pamela Burrage, Bevan ThompsonAbstract:In this paper we have used simulations to make a conjecture about the coverage of a t-dimensional subspace of a d-dimensional parameter space of size n when performing k trials of Latin Hypercube Sampling. This takes the form P(k,n,d,t) = 1 - e^(-k/n^(t-1)). We suggest that this coverage formula is independent of d and this allows us to make connections between building Populations of Models and Experimental Designs. We also show that Orthogonal Sampling is superior to Latin Hypercube Sampling in terms of allowing a more uniform coverage of the t-dimensional subspace at the sub-block size level. These ideas have particular relevance when attempting to perform uncertainty quantification and sensitivity analyses.
Diane Donovan - One of the best experts on this subject based on the ideXlab platform.
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estimates of the coverage of parameter space by latin hypercube and orthogonal array based Sampling
Applied Mathematical Modelling, 2017Co-Authors: Diane Donovan, Kevin Burrage, Pamela Burrage, T A Mccourt, Bevan Thompson, Emine şule YaziciAbstract:Abstract In this paper we use counting arguments to prove that the expected percentage coverage of a d dimensional parameter space of size n when performing k trials with either Latin Hypercube Sampling or Orthogonal Array-based Latin Hypercube Sampling is the same. We then extend these results to an experimental design setting by projecting onto a t
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populations of models experimental designs and coverage of parameter space by latin hypercube and orthogonal Sampling
arXiv: Methodology, 2015Co-Authors: Diane Donovan, Kevin Burrage, Pamela Burrage, Bevan ThompsonAbstract:In this paper we have used simulations to make a conjecture about the coverage of a $t$ dimensional subspace of a $d$ dimensional parameter space of size $n$ when performing $k$ trials of Latin Hypercube Sampling. This takes the form $P(k,n,d,t)=1-e^{-k/n^{t-1}}$. We suggest that this coverage formula is independent of $d$ and this allows us to make connections between building Populations of Models and Experimental Designs. We also show that Orthogonal Sampling is superior to Latin Hypercube Sampling in terms of allowing a more uniform coverage of the $t$ dimensional subspace at the sub-block size level.
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populations of models experimental designs and coverage of parameter space by latin hypercube and orthogonal Sampling
International Conference on Conceptual Structures, 2015Co-Authors: Diane Donovan, Kevin Burrage, Pamela Burrage, Bevan ThompsonAbstract:In this paper we have used simulations to make a conjecture about the coverage of a t-dimensional subspace of a d-dimensional parameter space of size n when performing k trials of Latin Hypercube Sampling. This takes the form P(k,n,d,t) = 1 - e^(-k/n^(t-1)). We suggest that this coverage formula is independent of d and this allows us to make connections between building Populations of Models and Experimental Designs. We also show that Orthogonal Sampling is superior to Latin Hypercube Sampling in terms of allowing a more uniform coverage of the t-dimensional subspace at the sub-block size level. These ideas have particular relevance when attempting to perform uncertainty quantification and sensitivity analyses.
Yan Chen - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Load Flow Method Based on Nataf Transformation and Latin Hypercube Sampling
IEEE Transactions on Sustainable Energy, 2013Co-Authors: Yan Chen, Jinyu Wen, Shijie ChengAbstract:This paper proposed a probabilistic load flow method that can address the correlated power sources and loads. The proposed probabilistic load flow method is based on the Nataf transformation and the Latin Hypercube Sampling. The main advantage of the proposed method is that high accurate solution can be obtained with less computation. Also, it is almost unconstrained for the probability distributions of the input random variables. Considering the uncertainties of correlated wind power, solar energy and loads, the effectiveness and the accuracy of the proposed probabilistic load flow method are verified by the comparative tests in a modified IEEE 14-bus system and a modified IEEE 118-bus system.