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The Handbook of Dynamic Data Driven Applications Systems
establishes an authoritative reference of DDDAS, pioneered by Dr.
Darema and the co-authors for researchers and practitioners
developing DDDAS technologies. Beginning with general concepts and
history of the paradigm, the text provides 32 chapters by leading
experts in ten application areas to enable an accurate
understanding, analysis, and control of complex systems; be they
natural, engineered, or societal: The authors explain how DDDAS
unifies the computational and instrumentation aspects of an
application system, extends the notion of Smart Computing to span
from the high-end to the real-time data acquisition and control,
and manages Big Data exploitation with high-dimensional model
coordination. The Dynamically Data Driven Applications Systems
(DDDAS) paradigm inspired research regarding the prediction of
severe storms. Specifically, the DDDAS concept allows
atmospheric observing systems, computer forecast models, and
cyberinfrastructure to dynamically configure themselves in optimal
ways in direct response to current or anticipated weather
conditions. In so doing, all resources are used in an
optimal manner to maximize the quality and timeliness of
information they provide.
Kelvin Droegemeier, Regents’ Professor of Meteorology at the
University of Oklahoma; former Director of the White House Office
of Science and Technology Policy
We may well be entering the golden age of
data science, as society in general has come to appreciate the
possibilities for organizational strategies that harness massive
streams of data. The challenges and opportunities are even greater
when the data or the underlying system are dynamic - and DDDAS is
the time-tested paradigm for realizing this potential.
Sangtae Kim, Distinguished Professor of Mechanical Engineering and
Distinguished Professor of Chemical Engineering at Purdue
University
The Handbook of Dynamic Data Driven Applications Systems
establishes an authoritative reference of DDDAS, pioneered by Dr.
Darema and the co-authors for researchers and practitioners
developing DDDAS technologies. Beginning with general concepts and
history of the paradigm, the text provides 32 chapters by leading
experts in ten application areas to enable an accurate
understanding, analysis, and control of complex systems; be they
natural, engineered, or societal: The authors explain how DDDAS
unifies the computational and instrumentation aspects of an
application system, extends the notion of Smart Computing to span
from the high-end to the real-time data acquisition and control,
and manages Big Data exploitation with high-dimensional model
coordination. The Dynamically Data Driven Applications Systems
(DDDAS) paradigm inspired research regarding the prediction of
severe storms. Specifically, the DDDAS concept allows atmospheric
observing systems, computer forecast models, and
cyberinfrastructure to dynamically configure themselves in optimal
ways in direct response to current or anticipated weather
conditions. In so doing, all resources are used in an optimal
manner to maximize the quality and timeliness of information they
provide. Kelvin Droegemeier, Regents' Professor of Meteorology at
the University of Oklahoma; former Director of the White House
Office of Science and Technology Policy We may well be entering the
golden age of data science, as society in general has come to
appreciate the possibilities for organizational strategies that
harness massive streams of data. The challenges and opportunities
are even greater when the data or the underlying system are dynamic
- and DDDAS is the time-tested paradigm for realizing this
potential. Sangtae Kim, Distinguished Professor of Mechanical
Engineering and Distinguished Professor of Chemical Engineering at
Purdue University
This Second Volume in the series Handbook of
Dynamic Data Driven Applications
Systems (DDDAS) expands the scope of the methods and
the application areas presented in the first Volume and aims
to provide additional and extended content of the increasing set of
science and engineering advances for new capabilities enabled
through DDDAS. The methods and examples of breakthroughs
presented in the book series capture the DDDAS paradigm and its
scientific and technological impact and benefits. The DDDAS
paradigm and the ensuing DDDAS-based frameworks for
systems’ analysis and design have been shown to engender new and
advanced capabilities for understanding, analysis, and management
of engineered, natural, and societal systems (“applications
systems”), and for the commensurate wide set of scientific and
engineering fields and applications, as well as foundational areas.
The DDDAS book series aims to be a reference source of many of the
important research and development efforts conducted under the
rubric of DDDAS, and to also inspire the broader communities of
researchers and developers about the potential in their respective
areas of interest, of the application and the exploitation of the
DDDAS paradigm and the ensuing frameworks, through the examples and
case studies presented, either within their own field or other
fields of study. As in the first volume, the chapters
in this book reflect research work conducted over the years
starting in the 1990’s to the present. Here, the theory
and application content are considered for: Foundational Methods
Materials Systems Structural Systems Energy Systems Environmental
Systems: Domain Assessment & Adverse Conditions/Wildfires
Surveillance Systems Space Awareness Systems Healthcare Systems
Decision Support Systems Cyber Security Systems Design of Computer
Systems The readers of this book series will benefit from
DDDAS theory advances such as object estimation, information
fusion, and sensor management. The increased interest in Artificial
Intelligence (AI), Machine Learning and Neural Networks (NN)
provides opportunities for DDDAS-based methods to show the key role
DDDAS plays in enabling AI capabilities; address challenges
that ML-alone does not, and also show how ML in combination
with DDDAS-based methods can deliver the advanced capabilities
sought; likewise, infusion of DDDAS-like approaches in NN-methods
strengthens such methods. Moreover, the “DDDAS-based Digital
Twin” or “Dynamic Digital Twin”, goes beyond the traditional
DT notion where the model and the physical system are viewed
side-by-side in a static way, to a paradigm where the model
dynamically interacts with the physical system through its
instrumentation, (per the DDDAS feed-back control loop between
model and instrumentation).
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Dynamic Data Driven Applications Systems - Third International Conference, DDDAS 2020, Boston, MA, USA, October 2-4, 2020, Proceedings (Paperback, 1st ed. 2020)
Frederica Darema, Erik Blasch, Sai Ravela, Alex Aved
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R1,582
Discovery Miles 15 820
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the Third
International Conference on Dynamic Data Driven Application
Systems, DDDAS 2020, held in Boston, MA, USA, in October 2020. The
21 full papers and 14 short papers presented in this volume were
carefully reviewed and selected from 40 submissions. They cover
topics such as: digital twins; environment cognizant
adaptive-planning systems; energy systems; materials systems;
physics-based systems analysis; imaging methods and systems; and
learning systems.
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Dynamic Data-Driven Environmental Systems Science - First International Conference, DyDESS 2014, Cambridge, MA, USA, November 5-7, 2014, Revised Selected Papers (Paperback, 1st ed. 2015)
Sai Ravela, Adrian Sandu
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R2,653
Discovery Miles 26 530
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the First
International Conference on Dynamic Data-Driven Environmental
Systems Science, DyDESS 2014, held in Cambridge, MA, USA, in
November 2014.The 24 revised full papers and 7 short papers were
carefully reviewed and selected from 62 submissions and cover
topics on sensing, imaging and retrieval for the oceans,
atmosphere, space, land, earth and planets that is informed by the
environmental context; algorithms for modeling and simulation,
downscaling, model reduction, data assimilation, uncertainty
quantification and statistical learning; methodologies for planning
and control, sampling and adaptive observation, and efficient
coupling of these algorithms into information-gathering and
observing system designs; and applications of methodology to
environmental estimation, analysis and prediction including
climate, natural hazards, oceans, cryosphere, atmosphere, land,
space, earth and planets.
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