Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning

Search missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current methods for information gathering, such as Monte Carlo Tree Search, are capable of reasoning over long horizons, but they are computationally expensive. An alternative...

Multilevel Monte Carlo with Surrogate Models for Resource Adequacy Assessment

Monte Carlo simulation is often used for the reliability assessment of power systems, but it converges slowly when the system is complex. Multilevel Monte Carlo (MLMC) can be applied to speed up computation without compromises on model complexity and accuracy that are limiting real-world effectiveness. In MLMC, models with different complexity...

Service life prediction of concrete structures under chloride environment based on Monte Carlo method

Transport of chlorides is one of the most critical factors determining the service life of concrete structures under ocean environments, and diffusion is usually regarded as the major mechanism for the transport of chloride ions into concrete. As chloride diffusivity is affected by temperature, most existing service life prediction models use...

Understanding the influence of 3D sidewall roughness on observed line-edge roughness in scanning electron microscopy images

Line-edge roughness (LER) is often measured from top-down critical dimension scanning electron microscope (CD-SEM) images. The true three-dimensional roughness profile of the sidewall is typically ignored in such analyses. We study the response of a CD-SEM to sidewall roughness (SWR) by simulation. We generate random rough lines and spaces,...

Simulation of shotnoise induced side-wall roughness in electron lithography

We have developed a fast three dimensional Monte-Carlo framework for the investigation of shotnoise induced side-wall roughness (SWR) formation. The calculation outline is demonstrated by an example for an exposure of a 100nm thick layer of negative tone resist (NTR) resist on top of an infinitely thick silicon substrate. We use our home...

Data Assimilation in Discrete Event Simulations: A Rollback Based Sequential Monte Carlo Approach

Data assimilation is an analysis technique which aims to incorporate measured observations into a dynamic system model in order to produce accurate estimates of the current state variables of the system. Although data assimilation is conventionally applied in continuous system models, it is also a desired ability for its discrete event...

Reliability of Percussion Drilling as a Mean to Define Rock Surface

In Finnish ground investigation methods percussion drilling has an important role as a mean to define the rock surface. Ground conditions in Finland vary a lot, however, it is quite common that the soil layers are relatively thin and the rock surface lies in around 3...5 meters depth. Besides that the rock is usually very hard, so it has to be...

Importance sampling of severe wind gusts

An important problem that arises during the design of wind turbines is estimating extreme loads with sufficient accuracy. This is especially difficult during iterative design phases when computational resources are scarce. Over the years, many methods have been proposed to extrapolate extreme load distributions from relatively short time series...

Error propagation in the computation of volumes in 3D city models with the Monte Carlo method

This paper describes the analysis of the propagation of positional uncertainty in 3D city models to the uncertainty in the computation of their volumes. Current work related to error propagation in GIS is limited to 2D data and 2D GIS operations, especially of rasters. In this research we have (1) developed two engines, one that generates random...

A new Eulerian Monte Carlo method for the joint velocity-scalar PDF equations in turbulent flows

In the field of turbulent combustion, Lagrangian Monte Carlo (LMC) methods (Pope, 85) have become an essential component of the probability density function (PDF) approach. LMC methods are based on stochastic particles, which evolve from prescribed stochastic ordinary differential equations (SODEs). They are used to compute the one-point...