scholarly journals Conditional stochastic simulation for character animation

2010 ◽  
pp. n/a-n/a
Author(s):  
N. Courty ◽  
A. Cuzol
Keyword(s):  
Stochastic Simulation ◽  
Character Animation ◽  
Conditional Stochastic Simulation

Multivariate conditional stochastic simulation of soil heterotrophic respiration at plot scale

Geoderma ◽  
2010 ◽  
Vol 160 (1) ◽  
pp. 74-82 ◽  
Author(s):  
M. Herbst ◽  
N. Prolingheuer ◽  
A. Graf ◽  
J.A. Huisman ◽  
L. Weihermüller ◽  
...  
Keyword(s):  
Stochastic Simulation ◽  
Heterotrophic Respiration ◽  
Soil Heterotrophic Respiration ◽  
Conditional Stochastic Simulation

scholarly journals Topographic uncertainty quantification for flow-like landslide models via stochastic simulations

2020 ◽  
Vol 20 (5) ◽  
pp. 1441-1461
Author(s):  
Hu Zhao ◽  
Julia Kowalski
Keyword(s):  
Stochastic Simulation ◽  
Computational Models ◽  
Extreme Values ◽  
Stochastic Simulations ◽  
Systematic Bias ◽  
Potential Hazard ◽  
Landslide Event ◽  
Dem Uncertainty ◽  
The Impact ◽  
Conditional Stochastic Simulation

Abstract. Digital elevation models (DEMs) representing topography are an essential input for computational models capable of simulating the run-out of flow-like landslides. Yet, DEMs are often subject to error, a fact that is mostly overlooked in landslide modeling. We address this research gap and investigate the impact of topographic uncertainty on landslide run-out models. In particular, we will describe two different approaches to account for DEM uncertainty, namely unconditional and conditional stochastic simulation methods. We investigate and discuss their feasibility, as well as whether DEM uncertainty represented by stochastic simulations critically affects landslide run-out simulations. Based upon a historic flow-like landslide event in Hong Kong, we present a series of computational scenarios to compare both methods using our modular Python-based workflow. Our results show that DEM uncertainty can significantly affect simulation-based landslide run-out analyses, depending on how well the underlying flow path is captured by the DEM, as well as on further topographic characteristics and the DEM error's variability. We further find that, in the absence of systematic bias in the DEM, a performant root-mean-square-error-based unconditional stochastic simulation yields similar results to a computationally intensive conditional stochastic simulation that takes actual DEM error values at reference locations into account. In all other cases the unconditional stochastic simulation overestimates the variability in the DEM error, which leads to an increase in the potential hazard area as well as extreme values of dynamic flow properties.

Comparison of geostatistical approaches dealing with the distribution of snow

2012 ◽  
Vol 4 (4) ◽  
Author(s):  
Marketa Prusova ◽  
Lucie Orlikova ◽  
Marketa Hanzlova
Keyword(s):  
Stochastic Simulation ◽  
Interpolation Method ◽  
Input Parameter ◽  
Real Data ◽  
Optimal Interpolation ◽  
Simulation Based ◽  
Geomorphological Parameters ◽  
Regionalized Variable ◽  
Basic Statistical Analysis ◽  
Conditional Stochastic Simulation

AbstractThis paper deals with a stochastic simulation. Snow cover, representing a regionalized variable, was studied and used as an input parameter for a stochastic simulation. The first step included basic statistical analysis of individual parameters of snow, e.g. snow height. In the next step, an analysis of relationships between the snow and the geomorphological parameters (altitude, slope and aspect) was conducted. The most current methods of spatial interpolation and multifactor evaluation are based on weighted regression relationships. Primarily, the use of conditional stochastic simulation was tested in a variety of software. The main aim of this investigation is to compare selected interpolation methods with stochastic simulation, based on the development of the values and on the evaluation of the incidence of extreme events. The study shall provide users with recommendations for selecting the optimal interpolation method and its application to real data.

scholarly journals Topographic uncertainty quantification for flow-like landslide models via stochastic simulations

2020 ◽  
Author(s):  
Hu Zhao ◽  
Julia Kowalski
Keyword(s):  
Stochastic Simulation ◽  
Computational Models ◽  
Extreme Values ◽  
Stochastic Simulations ◽  
Systematic Bias ◽  
Potential Hazard ◽  
Landslide Event ◽  
Dem Uncertainty ◽  
The Impact ◽  
Conditional Stochastic Simulation

Abstract. Topography representing digital elevation models (DEMs) are essential inputs for computational models capable of simulating the run-out of flow-like landslides. Yet, DEMs are often subject to error, a fact that is mostly overlooked in landslide modeling. We address this research gap and investigate the impact of topographic uncertainty on landslide-run-out models. In particular, we will describe two different approaches to account for DEM uncertainty, namely unconditional and conditional stochastic simulation methods. We investigate and discuss their feasibility, as well as whether DEM uncertainty represented by stochastic simulations critically affects landslide run-out simulations. Based upon a historic flow-like landslide event in Hong Kong, we present a series of computational scenarios to compare both methods using our modular Python-based workflow. Our results show that DEM uncertainty can significantly affect simulation-based landslide run-out analyses, depending on how well the underlying flow path is captured by the DEM, as well as further topographic characteristics and the DEM error's variability. We further find that in the absence of systematic bias in the DEM, a performant root mean square error based unconditional stochastic simulation yields similar results than a computationally intensive conditional stochastic simulation that takes actual DEM error values at reference locations into account. In all other cases the unconditional stochastic simulation overestimates the variability of the DEM error, which leads to an increase of the potential hazard area as well as extreme values of dynamic flow properties.

Stochastic simulation of cell cycle

2012 ◽  
Vol 1 (1) ◽  
Author(s):  
P. Luvsantseren ◽  
K. Lochin ◽  
E. Purevjav
Keyword(s):  
Cell Cycle ◽  
Stochastic Simulation
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