7,558 research outputs found
Emulating dynamic non-linear simulators using Gaussian processes
The dynamic emulation of non-linear deterministic computer codes where the
output is a time series, possibly multivariate, is examined. Such computer
models simulate the evolution of some real-world phenomenon over time, for
example models of the climate or the functioning of the human brain. The models
we are interested in are highly non-linear and exhibit tipping points,
bifurcations and chaotic behaviour. However, each simulation run could be too
time-consuming to perform analyses that require many runs, including
quantifying the variation in model output with respect to changes in the
inputs. Therefore, Gaussian process emulators are used to approximate the
output of the code. To do this, the flow map of the system under study is
emulated over a short time period. Then, it is used in an iterative way to
predict the whole time series. A number of ways are proposed to take into
account the uncertainty of inputs to the emulators, after fixed initial
conditions, and the correlation between them through the time series. The
methodology is illustrated with two examples: the highly non-linear dynamical
systems described by the Lorenz and Van der Pol equations. In both cases, the
predictive performance is relatively high and the measure of uncertainty
provided by the method reflects the extent of predictability in each system
Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model
We present a common framework for Bayesian emulation methodologies for multivariate-output simulators, or computer models, that employ either parametric linear models or nonparametric Gaussian processes. Novel diagnostics suitable for multivariate covariance-separable emulators are developed and techniques to improve the adequacy of an emulator are discussed and implemented. A variety of emulators are compared for a humanitarian relief simulator, modelling aid missions to Sicily after a volcanic eruption and earthquake, and a sensitivity analysis is conducted to determine the sensitivity of the simulator output to changes in the input variables. The results from parametric and nonparametric emulators are compared in terms of prediction accuracy, uncertainty quantification and scientific interpretability
A Distributed and Privacy-Aware Speed Advisory System for Optimising Conventional and Electric Vehicles Networks
One of the key ideas to make Intelligent Transportation Systems (ITS) work
effectively is to deploy advanced communication and cooperative control
technologies among the vehicles and road infrastructures. In this spirit, we
propose a consensus-based distributed speed advisory system that optimally
determines a recommended common speed for a given area in order that the group
emissions, or group battery consumptions, are minimised. Our algorithms achieve
this in a privacy-aware manner; namely, individual vehicles do not reveal
in-vehicle information to other vehicles or to infrastructure. A mobility
simulator is used to illustrate the efficacy of the algorithm, and
hardware-in-the-loop tests involving a real vehicle are given to illustrate
user acceptability and ease of the deployment.Comment: This is a journal paper based on the conference paper "Highway speed
limits, optimised consensus, and intelligent speed advisory systems"
presented at the 3rd International Conference on Connected Vehicles and Expo
(ICCVE 2014) in November 2014. This is the revised version of the paper
recently submitted to the IEEE Transactions on Intelligent Transportation
Systems for publicatio
Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks
Computational complexity has been the bottleneck of applying physically-based
simulations on large urban areas with high spatial resolution for efficient and
systematic flooding analyses and risk assessments. To address this issue of
long computational time, this paper proposes that the prediction of maximum
water depth rasters can be considered as an image-to-image translation problem
where the results are generated from input elevation rasters using the
information learned from data rather than by conducting simulations, which can
significantly accelerate the prediction process. The proposed approach was
implemented by a deep convolutional neural network trained on flood simulation
data of 18 designed hyetographs on three selected catchments. Multiple tests
with both designed and real rainfall events were performed and the results show
that the flood predictions by neural network uses only 0.5 % of time comparing
with physically-based approaches, with promising accuracy and ability of
generalizations. The proposed neural network can also potentially be applied to
different but relevant problems including flood predictions for urban layout
planning
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