Results 181 to 190 of about 2,168,026 (221)
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Journal of Nuclear Science and Technology, 2021
Dynamic probabilistic risk assessment (PRA), which handles epistemic and aleatory uncertainties by coupling the thermal-hydraulics simulation and probabilistic sampling, enables a more realistic and detailed analysis than conventional PRA.
K. Kubo +3 more
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Dynamic probabilistic risk assessment (PRA), which handles epistemic and aleatory uncertainties by coupling the thermal-hydraulics simulation and probabilistic sampling, enables a more realistic and detailed analysis than conventional PRA.
K. Kubo +3 more
semanticscholar +1 more source
Multilevel quasi-Monte Carlo for optimization under uncertainty
Numerische Mathematik, 2021This paper considers the problem of optimizing the average tracking error for an elliptic partial differential equation with an uncertain lognormal diffusion coefficient.
Philipp A. Guth, A. Barel
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Dynamic Testing for Autonomous Vehicles Using Random Quasi Monte Carlo
IEEE Transactions on Intelligent VehiclesThe substantial resource usage required to create ample scenarios for testing Autonomous Vehicles (AV) presents a bottleneck in their implementation.
Jingwei Ge +6 more
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Monte Carlo, Quasi-Monte Carlo, and Randomized Quasi-Monte Carlo
2000This paper surveys recent research on using Monte Carlo techniques to improve quasi-Monte Carlo techniques. Randomized quasi-Monte Carlo methods provide a basis for error estimation. They have, in the special case of scrambled nets, also been observed to improve accuracy.
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Grid-based Quasi-Monte Carlo Applications
Monte Carlo Methods and Applications, 2005Summary: We extend the techniques used in grid-based Monte Carlo applications to grid-based quasi-Monte Carlo applications. These techniques include an \(N\)-out-of-\(M\) strategy for efficiently scheduling subtasks on the grid, lightweight checkpointing for grid subtask status recovery, a partial result validation scheme to verify the correctness of ...
Yaohang Li, Michael Mascagni
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Quasi-Monte Carlo ray tracing algorithm for radiative flux distribution simulation
, 2020Monte Carlo ray tracing (MCRT) is a fundamental simulation method for central receiver systems(CRSs). MCRT is an effective method to describe the radiative flux distribution on the receiver surface reflected by either a single heliostat or all heliostats
Xiaoyue Duan +4 more
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Zufall und Quasi-Monte Carlo Ansätze / Randomness and Quasi-Monte Carlo Approaches
Jahrbücher für Nationalökonomie und Statistik, 1999Zusammenfassung Monte Carlo Methoden haben sich auf vielen Gebieten der Statistik und Ökonometrie als wertvolles Instrument erwiesen. Die übliche Verwendung von Pseudozufallszahlen führt dazu, daß der Zusammenhang zwischen einem allgemeinen Zufallsbegriff und der Anwendung in Monte Carlo Verfahren eher ein lockerer ist.
Peter Winker, Kai-Tai Fang
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IEEE Transactions on Industrial Informatics, 2019
The irregular and truncated probabilistic characteristics of wind power uncertainty lead to unknown influences on the power system operation. In this article, we propose a new probabilistic optimal power flow (POPF) framework, which can cope with such ...
Wei-Gao Sun +3 more
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The irregular and truncated probabilistic characteristics of wind power uncertainty lead to unknown influences on the power system operation. In this article, we propose a new probabilistic optimal power flow (POPF) framework, which can cope with such ...
Wei-Gao Sun +3 more
semanticscholar +1 more source
On Monte Carlo and Quasi-Monte Carlo for Matrix Computations
2018This paper focuses on minimizing further the communications in Monte Carlo methods for Linear Algebra and thus improving the overall performance. The focus is on producing set of small number of covering Markov chains which are much longer that the usually produced ones.
Vassil Alexandrov 0001 +5 more
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2004
This chapter discusses alternatives to Monte Carlo simulation known as quasi-Monte Carlo or low-discrepancy methods. These methods differ from ordinary Monte Carlo in that they make no attempt to mimic randomness. Indeed, they seek to increase accuracy specifically by generating points that are too evenly distributed to be random.
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This chapter discusses alternatives to Monte Carlo simulation known as quasi-Monte Carlo or low-discrepancy methods. These methods differ from ordinary Monte Carlo in that they make no attempt to mimic randomness. Indeed, they seek to increase accuracy specifically by generating points that are too evenly distributed to be random.
openaire +1 more source