Automating embedded analysis capabilities and managing software complexity in multiphysics simulation part II: application to partial differential equations

A template-based generic programming approach was presented in a previous paper that separates the development effort of programming a physical model from that of computing additional quantities, such as derivatives, needed for embedded analysis algorithms. In this paper, we describe the implementation details for using the template-based generic programming approach for simulation and analysis of partial differential equations (PDEs). We detail several of the hurdles that we have encountered, and some of the software infrastructure developed to overcome them. We end with a demonstration where we present shape optimization and uncertainty quantification results for a 3D PDE application

Automating embedded analysis capabilities and managing software complexity in multiphysics simulation part I: template-based generic programming

An approach for incorporating embedded simulation and analysis capabilities in complex simulation codes through template-based generic programming is presented. This approach relies on templating and operator overloading within the C++ language to transform a given calculation into one that can compute a variety of additional quantities that are necessary for many state-of-the-art simulation and analysis algorithms. An approach for incorporating these ideas into complex simulation codes through general graph-based assembly is also presented. These ideas have been implemented within a set of packages in the Trilinos framework and are demonstrated on a simple problem from chemical engineering

Interactive situation modelling in knowledge intensive domains

Interactive Situation Modelling (ISM) method, a semi-methodological approach, is proposed to tackle issues associated with modelling complex knowledge intensive domains, which cannot be easily modelled using traditional approaches. This paper presents the background and implementation of ISM within a complex domain, where synthesizing knowledge from various sources is critical, and is based on the principles of ethnography within a constructivist framework. Although the motivation for the reported work comes from the application presented in the paper, the actual scope of the paper covers a wide range of issues related to modelling complex systems. The author firstly reviews approaches used for modelling knowledge intensive domains, preceded by a brief discussion about two main issues: symmetry of ignorance and system behaviour, which are often confronted when applying modelling approaches to business domains. The ISM process is then characterized and critiqued with lessons from an exemplar presented to illustrate its effectiveness

Interactive situation modelling in knowledge intensive domains

Interactive Situation Modelling (ISM) method, a semi-methodological approach, is proposed to tackle issues associated with modelling complex knowledge intensive domains, which cannot be easily modelled using traditional approaches. This paper presents the background and implementation of ISM within a complex domain, where synthesizing knowledge from various sources is critical, and is based on the principles of ethnography within a constructivist framework. Although the motivation for the reported work comes from the application presented in the paper, the actual scope of the paper covers a wide range of issues related to modelling complex systems. The author firstly reviews approaches used for modelling knowledge intensive domains, preceded by a brief discussion about two main issues: symmetry of ignorance and system behaviour, which are often confronted when applying modelling approaches to business domains. The ISM process is then characterized and critiqued with lessons from an exemplar presented to illustrate its effectiveness.

The chaotic nature of healthcare information systems: The need for transdisciplinary collaboration

Copyright @ 2013 EMCIS.This paper demonstrates one of the challenges of the healthcare information systems development, namely the chaotic nature of healthcare systems. Although the reliable evidence demonstrating the positive effects of health information systems on safety and quality remains inconclusive (a growing body of research revealing the unintended consequences and potentially error producing effects of health information systems’ implementation. Different arguments from the literature concerning the chaotic nature of healthcare, including but not limited to the nature of patients and disease have been presented. The requirements of new ways of systems design and the need for transdisciplinary dynamic teams within the requirements engineering phase as a start has been discussed. These arguments have been investigated in the context of an exploratory case addressing one of the advanced oncology centres in the US. This paper concludes that there is an important need to rethink healthcare information systems development method, which has to be in a dynamic ongoing manner for some major issues

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

We introduce the \texttt{pyunicorn} (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, \texttt{pyunicorn} provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure