Relevant bibliographies by topics / Monte Carlo experiments

Academic literature on the topic 'Monte Carlo experiments'

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Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Monte Carlo experiments"

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Paxton, Pamela, Patrick J. Curran, Kenneth A. Bollen, Jim Kirby, and Feinian Chen. "Monte Carlo Experiments: Design and Implementation." Structural Equation Modeling: A Multidisciplinary Journal 8, no. 2 (April 2001): 287–312. http://dx.doi.org/10.1207/s15328007sem0802_7.

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Adkins, Lee C. "Using gretl for Monte Carlo experiments." Journal of Applied Econometrics 26, no. 5 (December 9, 2010): 880–85. http://dx.doi.org/10.1002/jae.1228.

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Demeler, Borries, and Emre Brookes. "Monte Carlo analysis of sedimentation experiments." Colloid and Polymer Science 286, no. 2 (June 13, 2007): 129–37. http://dx.doi.org/10.1007/s00396-007-1699-4.

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Norén, B., and B. Jakobsson. "Monte Carlo simulations of anomalon experiments." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 17, no. 3 (October 1986): 265–74. http://dx.doi.org/10.1016/0168-583x(86)90066-2.

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Anderson, Robert, Zhou Wei, Ian Cox, Malcolm Moore, and Florence Kussener. "Monte Carlo Simulation Experiments for Engineering Optimisation." Studies in Engineering and Technology 2, no. 1 (July 22, 2015): 97. http://dx.doi.org/10.11114/set.v2i1.901.

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Design of Experiments (DoE) is widely used in design, manufacturing and quality management. The resulting data is usually analysed with multiple linear regression to generate polynomial equations that describe the relationship between process inputs and outputs. These equations enable us to understand how input values affect the predicted value of one or more outputs and find good set points for the inputs. However, to develop robust manufacturing processes, we also need to understand how variation in these inputs appears as variation in the output. This understanding allows us to define set points and control tolerances for the inputs that will keep the outputs within their required specification windows. Tolerance analysis provides a powerful way of finding input settings and ranges that minimise output variation to produce a process that is robust. In many practical applications, tolerance analysis exploits Monte Carlo simulation of the polynomial model generated from DoE’s. This paper briefly describes tolerance analysis and then shows how Monte Carlo simulation experiments using space-filling designs can be used to find the input settings that result in a robust process. Using this approach, engineers can quickly and easily identify the key inputs responsible for transferring undesired variation to their process outputs and identify the set points and ranges that make their process as robust as possible. If the process is not sufficiently robust, they can rationally investigate different strategies to improve it. A case study approach is used to aid explanation and understanding.
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Zhang, Ji, and Dennis D. Boos. "Adjusted power estimates in monte carlo experiments." Communications in Statistics - Simulation and Computation 23, no. 1 (January 1994): 165–73. http://dx.doi.org/10.1080/03610919408813162.

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Leemis, Lawrence, and Bruce Schmeiser. "Random Variate Generation for Monte Carlo Experiments." IEEE Transactions on Reliability R-34, no. 1 (April 1985): 81–85. http://dx.doi.org/10.1109/tr.1985.5221941.

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Fishman, George S., and David S. Rubin. "Bounding the variance in Monte Carlo experiments." Operations Research Letters 11, no. 4 (May 1992): 243–48. http://dx.doi.org/10.1016/0167-6377(92)90031-w.

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Feng, Mingbin, and Jeremy Staum. "Green Simulation with Database Monte Carlo." ACM Transactions on Modeling and Computer Simulation 31, no. 1 (February 2021): 1–26. http://dx.doi.org/10.1145/3429336.

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In a setting in which experiments are performed repeatedly with the same simulation model, green simulation means reusing outputs from previous experiments to answer the question currently being asked of the model. In this article, we address the setting in which experiments are run to answer questions quickly, with a time limit providing a fixed computational budget, and then idle time is available for further experimentation before the next question is asked. The general strategy is database Monte Carlo for green simulation: the output of experiments is stored in a database and used to improve the computational efficiency of future experiments. In this article, the database provides a quasi-control variate, which reduces the variance of the estimated mean response in a future experiment that has a fixed computational budget. We propose a particular green simulation procedure using quasi-control variates, addressing practical issues such as experiment design, and analyze its theoretical properties. We show that, under some conditions, the variance of the estimated mean response in an experiment with a fixed computational budget drops to zero over a sequence of repeated experiments, as more and more idle time is invested in creating databases. Our numerical experiments on the procedure show that using idle time to create databases of simulation output provides variance reduction immediately, and that the variance reduction grows over time in a way that is consistent with the convergence analysis.
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Westland, Stephen, Yuan Li, and Vien Cheung. "Monte Carlo Analysis of Incomplete Paired-Comparison Experiments." Journal of Imaging Science and Technology 58, no. 5 (September 1, 2014): 505061–66. http://dx.doi.org/10.2352/j.imagingsci.technol.2014.58.5.050506.

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Dissertations / Theses on the topic "Monte Carlo experiments"

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Grinberg, Farida. "Ultraslow molecular dynamics of organized fluids: NMR experiments and Monte-Carlo simulations." Diffusion fundamentals 2 (2005) 119, S. 1-2, 2005. https://ul.qucosa.de/id/qucosa%3A14460.

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Ames, Allison Jennifer. "Monte Carlo Experiments on Maximum entropy Constructive Ensembles for Time Series Analysis and Inference." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/32571.

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In econometric analysis, the traditional bootstrap and related methods often require the assumption of stationarity. This assumption says that the distribution function of the process remains unchanged when shifted in time by an arbitrary value, imposing perfect time-homogeneity. In terms of the joint distribution, stationarity implies that the date of the first time index is not relevant. There are many problems with this assumption however for time series data. With time series, the order in which random realizations occur is crucial. This is why theorists work with stochastic processes, with two implicit arguments, w and t, where w represents the sample space and t represents the order. The question becomes, is there a bootstrap procedure that can preserve the ordering without assuming stationarity? The new method for maximum entropy ensembles proposed by Dr. H. D. Vinod might satisfy the Ergodic and Kolmogorov theorems, without assuming stationarity.
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Pettersson, Joachim. "Analysis of Monte Carlo data at low energies in electron-positron collider experiments using Initial State Radiation." Thesis, Uppsala universitet, Kärnfysik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-217038.

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This report explores a novel application of the initial state radiation (radiative return) method in an electron-positron collider, to measure the electron-positron annihilation into a neutral pion and a photon  reaction cross section at low energies. The challenge of using ISR events for analysis is due to the combinatorics issue presented by the extra photon(s) in the final state. Through measuring the cross section, access is gained to the time-like electromagnetic transition form factor (TFF) of the neutral pion. This can be used to constrain theoretical models of the hadronic light-by-light scattering contribution to the anomalous magnetic moment of the muon (AMM). The aim of this project was to determine if existing, or expected, data samples at the experiments KLOE-2 in Frascati and BES-III in Beijing could provide competitive results for the time-like TFF slope parameter. The analysis was performed through the construction of an event generator, where events were generated for three different reaction models. Considering the amount of data available at low energies, this study indicates that the ISR approach could be a viable option to enhance the data sample in the low energy region. The most promising experiment for further analysis is here indicated to be KLOE-2. Compared to tabulated values for the form factor slope parameter, the uncertainty retrieved here is roughly on the same order of magnitude or smaller.
I denna rapport behandlas en ny metod för analys av ISR-data från experiment vid elektron-positron-kolliderare, så som KLOE-2 och BES-III. Strålning i form av en eller flera fotoner som strålats ut från elektronen eller positronen innan kollision kallas ISR. Då en foton strålas ut  från initialtillståndet sänks reaktionens nominella energi. Detta möjliggör analys av reaktioner över ett kontinuerligt energispektrum. Utmaningen med ISR analys ligger i kombinatoriken som uppstår då det återfinns ytterligare fotoner i sluttillståndet för reaktionen.I rapporen beskrivs processen elektron-positron-annihilation till en neutral pion och en foton. Denna reaktion är intressant då kunskap om dess reaktionstvärsnitt ger tillgång till den elektromagnetiska formfaktorn för den neutrala pionen. Formfaktorn beskriver hur reaktionen i fråga avviker från en punkt-lik elektromagnetisk växelverkan. Den elektromagnetiska fromfaktorn för den neutrala pionen är i sin tur en viktig del i beräkningarna för det hadroniska bidraget till myonens anomala magnetiska moment (AMM). Eftersom AMM är experimentellt uppmätt till mycket god noggrannhet kan jämförelser med teoretiska modeller göras med hög precision.  Vid låg reaktionsenergi kan formfaktorn beskrivas med endast en parameter, lutningsparametern. Från Monte Carlo genererad ISR-data har i denna rapport lutningsparametern bestämts med noggrannhet som är likvärdig eller bättre än tabulerade värden, beroende på mängd analyserad data samt val av analysmetod.
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Lin, Heng. "CROSSOVER FROM UNENTANGLED TO ENTANGLED DYNAMICS: MONTE CARLO SIMULATION OF POLYETHYLENE, SUPPORTED BY NMR EXPERIMENTS." Akron, OH : University of Akron, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=akron1142028839.

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Dissertation (Ph. D.)--University of Akron, Dept. of Polymer Science, 2006.
"May, 2006." Title from electronic dissertation title page (viewed 10/11/2006) Advisor, Wayne L. Mattice; Committee members, Ernst D. von Meerwall, Ali Dhinojwala, Gustavo A. Carri, Richard J. Elliott; Department Chair, Mark D. Foster; Dean of the College, Frank N. Kelley; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.
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Schälicke, Andreas. "Event generation at hadron colliders." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2005. http://nbn-resolving.de/urn:nbn:de:swb:14-1122466458074-11492.

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Diese Arbeit befasst sich mit der Simulation von hochenergetischen Hadron-Kollisionsexperimenten, wie sie im Moment am Tevatron (Fermilab) durchgeführt werden und in naher Zukunft am Large Hadron Collider (LHC) am CERN zu erwarten sind. Für die Beschreibung dieser Experimente wird ein Algorithmus untersucht, der es ermöglicht, exakte Multijet-Matrixelemente auf Baumgraphenniveau in die Simulation einzubeziehen und so die Qualität der Vorhersage deutlich zu verbessern. Die Implementierung dieses Algorithmus in den Eventgenerator "SHERPA" und die Erweiterung des Parton Showers in diesem Programm ist das Hauptthema dieser Arbeit. Die Ergebnisse werden mit experimentellen Daten und mit anderen Simulationen verglichen
This work deals with the accurate simulation of high energy hadron-hadron-collision experiments, as they are currently performed at Fermilab Tevatron or as they are expected at the Large Hadron Collider at CERN. For a precise description of these experiments an algorithm is investigated, which enables the inclusion of exact multi-jet matrix elements in the simulation. The implementation of this algorithm in the event generator "SHERPA" and the extension of its parton shower is the main topic of this work. The results are compared with those of other simulation programs and with experimental data
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Moffat, Hayden. "Cost effective functional response experiments via sequential design." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/209917/1/Hayden_Moffat_Thesis.pdf.

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Functional response experiments are commonly used to explore predator-prey systems, where we are interested in learning about the number of prey consumed per predator as a function of prey density. Currently, functional response experiments are designed in an ad-hoc manner and may require significant experimentation to learn about the underlying system. In this thesis, we developed statistically principled functional response designs to learn about the true mathematical model driving the predator-prey dynamics as quickly as possible. This can lead to functional response experiments with reduced monetary costs and less sacrificing of animals.
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Chetvertkova, Vera [Verfasser], Edil [Akademischer Betreuer] Mustafin, Ulrich [Akademischer Betreuer] Ratzinger, and Oliver [Akademischer Betreuer] Kester. "Verification of Monte Carlo transport codes by activation experiments / Vera Chetvertkova. Gutachter: Ulrich Ratzinger ; Oliver Kester. Betreuer: Edil Mustafin." Frankfurt am Main : Univ.-Bibliothek Frankfurt am Main, 2013. http://d-nb.info/104409401X/34.

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Hatzinger, Reinhold, and Walter Katzenbeisser. "A Combination of Nonparametric Tests for Trend in Location." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1991. http://epub.wu.ac.at/1298/1/document.pdf.

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A combination of some well known nonparametric tests to detect trend in location is considered. Simulation results show that the power of this combination is remarkably increased. (author's abstract)
Series: Forschungsberichte / Institut für Statistik
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Lyubchyk, Andriy. "Gas adsorption in the MIL-53(AI) metal organic framework. Experiments and molecular simulation." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/10932.

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Dissertação para obtenção do Grau de Doutor em Engenharia Química
FCT - PhD Fellowship at Universidade Nova de Lisboa, Department of Chemistry (bolsa N SFRH/BD/45477/2008); FCT Program, project PTDC/AAC-AMB/108849/2008; NANO_GUARD, Project N°269138; Programme “PEOPLE” – Call ID “FP7-PEOPLE-2010-IRSES”
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Oliveira, José Benedito da Silva. "Combinação de técnicas de delineamento de experimentos e elementos finitos com a otimização via simulação Monte Carlo /." Guaratinguetá, 2019. http://hdl.handle.net/11449/183380.

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Orientador: Aneirson Francisco da Silva
Resumo: A Estampagem a Frio é um processo de conformação plástica de chapas metálicas, que possibilita, por meio de ferramentas específicas, obter componentes com boas propriedades mecânicas, geometrias e espessuras variadas, diferentes especificações de materiais e com boa vantagem econômica. A multiplicidade destas variáveis gera a necessidade de utilização de técnicas estatísticas e de simulação numérica, que suportem a sua análise e adequada tomada de decisão na elaboração do projeto das ferramentas de conformação. Este trabalho foi desenvolvido em uma empresa brasileira multinacional de grande porte que atua no setor de autopeças, em seu departamento de engenharia de projetos de ferramentas, com o propósito de reduzir o estiramento e a ocorrência de trincas em uma travessa de 6,8 [mm] de aço LNE 380. A metodologia proposta obtém os valores dos fatores de entrada e sua influência na variável resposta com o uso de técnicas de Delineamento de Experimentos (DOE) e simulação pelo método de Elementos Finitos (FE). Uma Função Empírica é desenvolvida a partir desses dados, com o uso da técnica de regressão, obtendo-se a variável resposta y (espessura na região crítica), em função dos fatores influentes xi do processo. Com a Otimização via Simulação Monte Carlo (OvSMC) insere-se a incerteza nos coeficientes desta Função Empírica, sendo esta a principal contribuição deste trabalho, pois é o que ocorre, por via de regra, na prática com problemas experimentais. Simulando-se por FE as ferram... (Resumo completo, clicar acesso eletrônico abaixo)
Mestre
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Books on the topic "Monte Carlo experiments"

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Sentā, Ōarai Kōgaku. Optimization of Monte Carlo methods for calculational predictions of dosimetry measurements in "Joyo" MK-III core: [kenkyū hōkoku]. Tokai-mura, Naka-gun, Ibaraki, Japan: Japan Nuclear Cycle Development Institute, 2005.

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KEK), EGS Users' Meeting in Japan (14th 2007. Proceedings of the Fourteenth EGS Users' Meeting in Japan: August 7-9, 2007, KEK, Tsukuba, Japan. Tsukuba-shi, Ibaraki-ken, Japan: High Energy Accelerator Research Organization, 2007.

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KEK), EGS Users' Meeting in Japan (15th 2008. Proceedings of the Fifteenth EGS Users' Meeting in Japan: August 6-8, 2008, KEK, Tsukuba, Japan. Tsukuba-shi, Ibaraki-ken, Japan: High Energy Accelerator Research Organization, 2008.

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EGS Users' Meeting in Japan (13th 2006 KEK). Proceedings of the Thirteenth EGS Users' Meeting in Japan: August 8-10, 2006, KEK, Tsukuba, Japan. Tsukuba-shi, Ibaraki-ken, Japan: High Energy Accelerator Research Organization, 2006.

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EGS Users' Meeting in Japan (12th 2005 KEK). Proceedings of the Twelfth EGS Users' Meeting in Japan: August 9-11, 2005, KEK, Tsukuba, Japan. Tsukuba-shi, Ibaraki-ken, Japan: High Energy Accelerator Research Organization, 2005.

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Alvarez, Luis J. BVAR models in the context of cointegration: A Monte Carlo experiment. [Barcelona]: [ESADE], 1994.

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Würl, Matthias. Towards Offline PET Monitoring at a Cyclotron-Based Proton Therapy Facility: Experiments and Monte Carlo Simulations. Springer Spektrum, 2016.

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Würl, Matthias. Towards Offline PET Monitoring at a Cyclotron-Based Proton Therapy Facility: Experiments and Monte Carlo Simulations. Spektrum Akademischer Verlag GmbH, 2016.

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Grazia, Maria. Monte Carlo Simulation for Experimental Physics: A Practical Introduction to Concepts, Methods, Technology and Tools. Taylor & Francis Group, 2019.

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Direct simulation Monte Carlo prediction of on-orbit contaminant deposit levels for HALOE. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.

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Book chapters on the topic "Monte Carlo experiments"

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Vamos, Calin, and Maria Craciun. "Monte Carlo Experiments." In SpringerBriefs in Physics, 15–30. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4825-5_2.

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Tanizaki, Hisashi. "Monte-Carlo Experiments." In Nonlinear Filters, 113–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03223-7_5.

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Stanton, C. J., and D. W. Bailey. "Evaluating Photoexcitation Experiments Using Monte Carlo Simulations." In Monte Carlo Device Simulation, 67–97. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4026-7_3.

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Sinclair, Alistair. "Convergence Rates for Monte Carlo Experiments." In Numerical Methods for Polymeric Systems, 1–17. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1704-6_1.

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Kleijnen, Jack P. C. "Design and Analysis of Monte Carlo Experiments." In Handbook of Computational Statistics, 529–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21551-3_18.

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Tanizaki, Hisashi. "Comparison of Nonlinear Filters: Monte-Carlo Experiments." In Lecture Notes in Economics and Mathematical Systems, 97–126. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-22237-9_5.

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Kerm, Philippe. "Inference on Inequality Measures: A Monte Carlo Experiment." In Inequalities: Theory, Experiments and Applications, 283–306. Vienna: Springer Vienna, 2002. http://dx.doi.org/10.1007/978-3-7091-6166-1_11.

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Roe, Byron P. "The Monte Carlo Method: Computer Simulation of Experiments." In Probability and Statistics in Experimental Physics, 62–74. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4757-2186-7_8.

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Roe, Byron P. "The Monte Carlo Method: Computer Simulation of Experiments." In Probability and Statistics in Experimental Physics, 66–80. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4684-9296-5_8.

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Roe, Byron P. "The Monte Carlo Method: Computer Simulation of Experiments." In Probability and Statistics in the Physical Sciences, 71–83. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53694-7_8.

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Conference papers on the topic "Monte Carlo experiments"

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Hingston, P., and M. Masek. "Experiments with Monte Carlo Othello." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4425000.

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Bouzy, Bruno, and Guillaume Chaslot. "Monte-Carlo Go Reinforcement Learning Experiments." In 2006 IEEE Symposium on Computational Intelligence and Games. IEEE, 2006. http://dx.doi.org/10.1109/cig.2006.311699.

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Caminata, A. "Monte Carlo simulation in solar neutrino experiments." In 5th International Solar Neutrino Conference. WORLD SCIENTIFIC, 2019. http://dx.doi.org/10.1142/9789811204296_0026.

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Santos, Andre, Pedro A. Santos, and Francisco S. Melo. "Monte Carlo tree search experiments in hearthstone." In 2017 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 2017. http://dx.doi.org/10.1109/cig.2017.8080446.

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Curl, Ian. "Virtual experiments with the Monte Carlo code MCBEND." In Fifth International Conference on Applications of Nuclear Techniques: Neutrons in Research and Industry, edited by George Vourvopoulos. SPIE, 1997. http://dx.doi.org/10.1117/12.267946.

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Drosg, M., Marianne E. Hamm, and Robert W. Hamm. "Complete Monte Carlo Simulation of Neutron Scattering Experiments." In APPLICATIONS OF NUCLEAR TECHNIQUES: Eleventh International Conference. AIP, 2011. http://dx.doi.org/10.1063/1.3665300.

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Pohl, Martin. "SIMDET—a parametric Monte Carlo for a TESLA detector." In Physics and experiments with future linear e+ e- colliders. AIP, 2001. http://dx.doi.org/10.1063/1.1394403.

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Bruscaglioni, Piero, P. Poggi, Giovanni Macelloni, and Simonetta Paloscia. "Monte Carlo: an application to modeling remote sensing of vegetation - coherent and incoherent models." In Lidar Multiple Scattering Experiments, edited by Christian Werner, Ulrich G. Oppel, and Tom Rother. SPIE, 2003. http://dx.doi.org/10.1117/12.512327.

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Davidenko, G., M. A. Funk, V. Kim, N. Kuropatkin, V. Kurshetsov, V. Molchanov, S. Rud, L. Stutte, V. Verebryusov, and R. Zukanovich Funchal. "GE781: a Monte Carlo package for fixed target experiments." In Proceedings of the International Conference. WORLD SCIENTIFIC, 1996. http://dx.doi.org/10.1142/9789814447188_0148.

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Camberos, Jose, Robert Greendyke, Larry Lambe, and Brook Bentley. "Computational Experiments with Direct Simulation Quasi-Random Monte Carlo." In 41st AIAA Thermophysics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-3742.

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Reports on the topic "Monte Carlo experiments"

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Marchetti, A. A. ,. LLNL. New Monte Carlo simulations of the LLNL pulsed-sphere experiments. Office of Scientific and Technical Information (OSTI), July 1998. http://dx.doi.org/10.2172/304515.

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Politis, Dimitris N., Raffaella Giacomini, and Halbert White. A warp-speed method for conducting Monte Carlo experiments involving bootstrap estimators. Cemmap, May 2012. http://dx.doi.org/10.1920/wp.cem.2012.1112.

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Ayoul-Guilmard, Q., F. Nobile, S. Ganesh, M. Nuñez, R. Tosi, C. Soriano, and R. Rosi. D5.5 Report on the application of multi-level Monte Carlo to wind engineering. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.03.

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We study the use of multi-level Monte Carlo methods for wind engineering. This report brings together methodological research on uncertainty quantification and work on target applications of the ExaQUte project in wind and civil engineering. First, a multi-level Monte Carlo for the estimation of the conditional value at risk and an adaptive algorithm are presented. Their reliability and performance are shown on the time-average of a non-linear oscillator and on the lift coefficient of an airfoil, with both preset and adaptively refined meshes. Then, we propose an adaptive multi-fidelity Monte Carlo algorithm for turbulent fluid flows where multilevel Monte Carlo methods were found to be inefficient. Its efficiency is studied and demonstrated on the benchmark problem of quantifying the uncertainty on the drag force of a tall building under random turbulent wind conditions. All numerical experiments showcase the open-source software stack of the ExaQUte project for large-scale computing in a distributed environment.
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Kiedrowski, Brian C. Adjoint Weighting Methods Applied to Monte Carlo Simulations of Applications and Experiments in Nuclear Criticality. Office of Scientific and Technical Information (OSTI), March 2014. http://dx.doi.org/10.2172/1122890.

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Descalle, M., and J. Pruet. Nuclear data verification based on Monte Carlo simulations of the LLNL pulsed-sphere benchmark experiments (1979 & 1986) using the Mercury code. Office of Scientific and Technical Information (OSTI), June 2008. http://dx.doi.org/10.2172/945692.

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Choi, Myunghee, and Vincent Chan. SciDAC Center for Simulation of Wave-Plasma Interactions - Iterated Finite-Orbit Monte Carlo Simulations with Full-Wave Fields for Modeling Tokamak ICRF Wave Heating Experiments - Final Report. Office of Scientific and Technical Information (OSTI), February 2014. http://dx.doi.org/10.2172/1121628.

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Descalle, M., C. Clouse, and J. Pruet. Monte Carlo and deterministic simulations of activation ratio experiments for 238U(n,f), 238U(n,g) and 238U(n,2n) in the Big Ten benchmark critical assembly. Office of Scientific and Technical Information (OSTI), July 2009. http://dx.doi.org/10.2172/967294.

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Ejarque, J., R. Tosi, M. Nuñez, S. Böhm, R. Badia, C. Soriano, and R. Rossi. D4.5 Framework development and release. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.05.

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This deliverable presents the nal release of the ExaQUte framework as result of task 4.6 of the project focused on the framework development and optimization. The rst part of the document presents an overview of the dierent parts of the ExaQUte framework providing the links to the repositories where the code of the dierent components can be found as well as the installation and usage guidelines. These repositories will include the nal version of the ExaQUte API and its implementation for the runtimes provided in the project (PyCOMPSs/COMPSs and Quake). The second part of the document presents a performance analysis of the framework by performing strong and weak scaling experiments. In this case, we have focused on the analysis of the new features introduced during the last part of the project to support and optimize the execution of MPI solvers inside the framework. The support for OpenMP was already reported in Deliverable D4.3 [21]. The results of the experiments demonstrate that the proposed framework allow to reach very good scalability for the analysed Monte Carlo problems.
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Ayoul-Guilmard, Q., S. Ganesh, M. Nuñez, R. Tosi, F. Nobile, R. Rossi, and C. Soriano. D5.3 Report on theoretical work to allow the use of MLMC with adaptive mesh refinement. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.002.

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This documents describes several studies undertaken to assess the applicability of MultiLevel Monte Carlo (MLMC) methods to problems of interest; namely in turbulent fluid flow over civil engineering structures. Several numerical experiments are presented wherein the convergence of quantities of interest with mesh parameters are studied at different Reynolds’ numbers and geometries. It was found that MLMC methods could be used successfully for low Reynolds’ number flows when combined with appropriate Adaptive Mesh Refinement (AMR) strategies. However, the hypotheses for optimal MLMC performance were found to not be satisfied at higher turbulent Reynolds’ numbers despite the use of AMR strategies. Recommendations are made for future research directions based on these studies. A tentative outline for an MLMC algorithm with adapted meshes is made, as well as recommendations for alternatives to MLMC methods for cases where the underlying assumptions for optimal MLMC performance are not satisfied.
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Ayoul-Guilmard, Q., S. Ganesh, M. Nuñez, R. Tosi, F. Nobile, R. Rossi, and C. Soriano. D5.4 Report on MLMC for time dependent problems. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.005.

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In this report, we study the use of Multi-Level Monte Carlo (MLMC) methods for time dependent problems. It was found that the usability of MLMC methods depends strongly on whether or not the underlying time dependent problem is chaotic in nature. Numerical experiments are conducted on both simple problems, as well as fluid flow problems of practical interest to the ExaQUte project, to demonstrate this. For the non-chaotic cases, the hypotheses that enable the use of MLMC methods were found to be satisfied. For the chaotic cases, especially the case of high Reynolds’ number fluid flow, the hypotheses were not satisfied. However, it was found that correlations between the different levels were high enough to merit the use of multi-fidelity or control-variate approaches. It was also noted that MLMC methods could work for chaotic problems if the time window of analysis were chosen to be small enough. Future studies are proposed to examine this possibility.
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