Monte Carlo Simulation
By John A. Sokolowski. ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2010
881 citations recorded by the source corpus.
Subfields: Industrial and Manufacturing Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/0e57c7e3c15eeb465f521204b201b640d4240910
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Industrial and Manufacturing Engineering
- M. Skitmore
- Weisheng Lu
- Vadim Borisov
- Gjergji Kasneci
- Rohit Tripathy
- Ilias Bilionis
- Jiahui Shen
- Tobias Leemann
In Management Science and Operations Research
- M. Skitmore
- Weisheng Lu
- Vadim Borisov
- Gjergji Kasneci
- Rohit Tripathy
- Ilias Bilionis
- Jiahui Shen
- Tobias Leemann
Citations in the corpus
- The impact of urbanization on carbon emissions in developing countries: a Chinese study based on the U-Kaya method (2016, methodology): “Monte Carlo simulation is a numerical experimentation technique to obtain the statistics of the output variables of a system computational model based on their distribution statistics of the input variables (Mahadevan, 1997).”
- Deep Neural Networks and Tabular Data: A Survey (2021, methodology): “Monte Carlo simulations [193] were used to produce the data.”
- Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification (2018, methodology): “The simplest method for tackling the UP problem is the Monte Carlo (MC) method [3, 4, 5].”
- Theia: an advanced optical neutrino detector (2019, methodology): “A precision measurement of the neutrino flux from the CNO cycle could prove extremely useful in resolving the discrepancy, as the flux depends linearly on solar metallicity [78].”