162,230 research outputs found
Multi-level agent-based modeling - A literature survey
During last decade, multi-level agent-based modeling has received significant
and dramatically increasing interest. In this article we present a
comprehensive and structured review of literature on the subject. We present
the main theoretical contributions and application domains of this concept,
with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic
statistics updated. v7 Change of the name of the paper to reflect what it
became, many refs and text added, bibliographic statistics update
Physics-related epistemic uncertainties in proton depth dose simulation
A set of physics models and parameters pertaining to the simulation of proton
energy deposition in matter are evaluated in the energy range up to
approximately 65 MeV, based on their implementations in the Geant4 toolkit. The
analysis assesses several features of the models and the impact of their
associated epistemic uncertainties, i.e. uncertainties due to lack of
knowledge, on the simulation results. Possible systematic effects deriving from
uncertainties of this kind are highlighted; their relevance in relation to the
application environment and different experimental requirements are discussed,
with emphasis on the simulation of radiotherapy set-ups. By documenting
quantitatively the features of a wide set of simulation models and the related
intrinsic uncertainties affecting the simulation results, this analysis
provides guidance regarding the use of the concerned simulation tools in
experimental applications; it also provides indications for further
experimental measurements addressing the sources of such uncertainties.Comment: To be published in IEEE Trans. Nucl. Sc
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
History and development of validation with the ESP-r simulation program
It is well recognised that validation of dynamic building simulation programs is a long-term complex task. There have been many large national and international efforts that have led to a well-established validation methodology comprising analytical, inter-program comparison and empirical validation elements, and a significant number of tests have been developed. As simulation usage increases, driven by such initiatives as the European Energy Performance of Buildings Directive, such tests are starting to be incorporated into national and international standards. Although many program developers have run many of the developed tests, there does not appear to have been a systematic attempt to incorporate such tests into routine operation of the simulation programs. This paper reports work undertaken to address this deficiency. The paper summarizes the tests that have been applied to the simulation program ESP-r. These tests have been developed within International Energy Agency Annexes, within CEN standards, within various large-scale national projects, and by the UK's Chartered Institution of Building Services Engineers. The structure used to encapsulate the tests allows developers to ensure that recent code modifications have not resulted in unforeseen impacts on program predictions, and allows users to check for themselves against benchmarks
CFD Applications in Energy Engineering Research and Simulation: An Introduction to Published Reviews
Computational Fluid Dynamics (CFD) has been firmly established as a fundamental
discipline to advancing research on energy engineering. The major progresses achieved during the
last two decades both on software modelling capabilities and hardware computing power have
resulted in considerable and widespread CFD interest among scientist and engineers. Numerical
modelling and simulation developments are increasingly contributing to the current state of the art in
many energy engineering aspects, such as power generation, combustion, wind energy, concentrated
solar power, hydro power, gas and steam turbines, fuel cells, and many others. This review intends to
provide an overview of the CFD applications in energy and thermal engineering, as a presentation and
background for the Special Issue “CFD Applications in Energy Engineering Research and Simulation”
published by Processes in 2020. A brief introduction to the most significant reviews that have been
published on the particular topics is provided. The objective is to provide an overview of the CFD
applications in energy and thermal engineering, highlighting the review papers published on the
different topics, so that readers can refer to the different review papers for a thorough revision of the
state of the art and contributions into the particular field of interest
A Novel Chronic Disease Policy Model
We develop a simulation tool to support policy-decisions about healthcare for
chronic diseases in defined populations. Incident disease-cases are generated
in-silico from an age-sex characterised general population using standard
epidemiological approaches. A novel disease-treatment model then simulates
continuous life courses for each patient using discrete event simulation.
Ideally, the discrete event simulation model would be inferred from complete
longitudinal healthcare data via a likelihood or Bayesian approach. Such data
is seldom available for relevant populations, therefore an innovative approach
to evidence synthesis is required. We propose a novel entropy-based approach to
fit survival densities. This method provides a fully flexible way to
incorporate the available information, which can be derived from arbitrary
sources. Discrete event simulation then takes place on the fitted model using a
competing hazards framework. The output is then used to help evaluate the
potential impacts of policy options for a given population.Comment: 24 pages, 13 figures, 11 table
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