208,205 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
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
Dynamic state reconciliation and model-based fault detection for chemical processes
In this paper, we present a method for the fault detection based on the residual generation. The main idea is to reconstruct the outputs of the system from the measurements using the extended Kalman filter. The estimations are compared to the values of the reference model and so, deviations are interpreted as possible faults. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. The use of this method is illustrated through an application in the field of chemical processe
Virtual Communication Stack: Towards Building Integrated Simulator of Mobile Ad Hoc Network-based Infrastructure for Disaster Response Scenarios
Responses to disastrous events are a challenging problem, because of possible
damages on communication infrastructures. For instance, after a natural
disaster, infrastructures might be entirely destroyed. Different network
paradigms were proposed in the literature in order to deploy adhoc network, and
allow dealing with the lack of communications. However, all these solutions
focus only on the performance of the network itself, without taking into
account the specificities and heterogeneity of the components which use it.
This comes from the difficulty to integrate models with different levels of
abstraction. Consequently, verification and validation of adhoc protocols
cannot guarantee that the different systems will work as expected in
operational conditions. However, the DEVS theory provides some mechanisms to
allow integration of models with different natures. This paper proposes an
integrated simulation architecture based on DEVS which improves the accuracy of
ad hoc infrastructure simulators in the case of disaster response scenarios.Comment: Preprint. Unpublishe
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
Is problem solving, or simulation model solving, mission critical?
How do we consider problems and models in the practice of simulation? It is our possibly contentious observation that simulation model solving seems to be more critical to the mission of simulation modeling than problem solving. Inspired by the theme of this year's Winter Simulation Conference, we ask the question, "Is problem solving, or simulation model solving, mission critical?" To investigate this we look at three perspectives, those of the textbook, the article and the editorial. The textbook perspective is the balance of the "traditional" view of simulation presented by the academic textbook against practical experience. The article perspective is a classification of papers published in four leading simulation journals in the year 2004 (ACM TOMACS, SIMULATION, Simulation Modelling Practice and Theory, and Simulation & Gaming). The editorial perspective is a discussion of editorial policy presented by the same journals. Our findings show that our observation is not contradicted
Modelling Fresh Strawberry Supply "From-Farm-to-Fork" as a Complex Adaptive Network
 The purpose of this study is to model and thereby enable simulation of the complete business entity of fresh food supply. A case narrative of fresh strawberry supply provides basis for this modelling. Lamming et al. (2000) point to the importance of discerning industry-specific product features (or particularities) regarding managing supply networks when discussing elements in "an initial classification of a supply network" while Fisher (1997) and Christopher et al. (2006, 2009) point to the lack of adopting SCM models to variations in products and market types as an important source of SCM failure. In this study we have chosen to move along a research path towards developing an adapted approach to model end-to-end fresh food supply influenced by a combination of SCM, system dynamics and complex adaptive network thinking...
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