Geometrical Splitting in Monte Carlo

Dubi, A.; Elperin, T.; Dudziak, D.J.

doi:10.13182/nse82-a21411

Cited by 22 publications

(4 citation statements)

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“…Today, a diverse community of scientists uses rare event sampling and analysis tools to study processes that take place infrequently, are too complex to be described analytically, and can be simulated on a computer. For example, the following extraordinary events have all been simulated using rare event sampling: a high-energy particle penetrating a nuclear shield 6 , a life-sustaining protein-protein reaction 7 , and an extreme loss of portfolio value 8 .…”

Section: Introductionmentioning

confidence: 99%

Practical rare event sampling for extreme mesoscale weather

Webber

Plotkin

O’Neill

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Extreme mesoscale weather, including tropical cyclones, squall lines, and floods, can be enormously damaging and yet challenging to simulate; hence, there is a pressing need for more efficient simulation strategies. Here we present a new rare event sampling algorithm called Quantile Diffusion Monte Carlo (Quantile DMC). Quantile DMC is a simple-to-use algorithm that can sample extreme tail behavior for a wide class of processes. We demonstrate the advantages of Quantile DMC compared to other sampling methods and discuss practical aspects of implementing Quantile DMC. To test the feasibility of Quantile DMC for extreme mesoscale weather, we sample extremely intense realizations of two historical tropical cyclones, 2010 Hurricane Earl and 2015 Hurricane Joaquin. Our results demonstrate Quantile DMC's potential to provide low-variance extreme weather statistics while highlighting the work that is necessary for Quantile DMC to attain greater efficiency in future applications.

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Section: Introductionmentioning

confidence: 99%

Practical rare event sampling for extreme mesoscale weather

Webber

Plotkin

O’Neill

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…Dubi's work was focused on exponential transform [45,46] and importance splitting [47][48][49][50][51][52]. Burn extended Dubi's importance splitting work in generality [53,54] and to other variance-reduction techniques [55,56].…”

Section: Dubi and Burnmentioning

confidence: 99%

Cost-optimized Automated Variance Reduction for Highly Angle-dependent Radiation Transport Analyses [Dissertation]

2023

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I appreciate Ms. Peggy Jo Gramer's and Dr. Garnette Roberts's ceaseless work to ensure that all needs, academic and personal, were addressed during my time as a doctoral student.My gratitude also goes to mentors from earlier in my career, Mr. Stan Anderson, Mr. Arnie Fero, and Dr. Gianluca Longoni, for instilling strong engineering principles and for encouraging me to follow a path that lead me to this point.

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“…2 in the case of n disjunctive splitting surfaces is given by [14]: The expression for the second moment of the estimator defined in eq.…”

Section: Prediction Of the Second Moment And The Computational Benefitmentioning

confidence: 99%

Monte Carlo variance reduction in applications to Systems Reliability using Phase Space Splitting

Khazen

Dubi

2004

Monte Carlo Methods and Applications

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Splitting is a widely known Monte Carlo variance reduction method (VRM). It has been successfully applied for a long time in Monte Carlo applications to neutral particles transport in Nuclear Engineering. In this field splitting is usually referred to as a Geometric Splitting method since it relies on geometric properties of the medium, namely distance and direction. Splitting is remarkable in the sense that it is a "safe" method. Unlike VRMs based on biasing it can not cause the sow tooth phenomenon in which a wrong result appears with very small statistical error. The only risk of using this method is increasing computation time. This makes the application of this method to such areas as safety-critical systems reliability and risk assessment a very attractive option.Suggested work concerns application of splitting to systems reliability problems. The system's structure based metric is used instead of the geometric approach. This metric takes into account importance of each system's component for the target event. Multiple disjunctive surfaces splitting, and splitting combined with the Cluster-Event biasing are considered. The optimal splitting parameters are predicted using the mathematical apparatus developed for applications of splitting in neutral particles transport. Provided numerical examples show considerable benefit of the method.

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Geometrical Splitting in Monte Carlo

Cited by 22 publications

References 0 publications

Practical rare event sampling for extreme mesoscale weather

Practical rare event sampling for extreme mesoscale weather

Cost-optimized Automated Variance Reduction for Highly Angle-dependent Radiation Transport Analyses [Dissertation]

Monte Carlo variance reduction in applications to Systems Reliability using Phase Space Splitting

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