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This book is a collection of selected papers presented at the 10th
International Conference on Scientific Computing in Electrical
Engineering (SCEE), held in Wuppertal, Germany in 2014. The book is
divided into five parts, reflecting the main directions of SCEE
2014: 1. Device Modeling, Electric Circuits and Simulation, 2.
Computational Electromagnetics, 3. Coupled Problems, 4. Model Order
Reduction, and 5. Uncertainty Quantification. Each part starts with
a general introduction followed by the actual papers. The aim of
the SCEE 2014 conference was to bring together scientists from
academia and industry, mathematicians, electrical engineers,
computer scientists, and physicists, with the goal of fostering
intensive discussions on industrially relevant mathematical
problems, with an emphasis on the modeling and numerical simulation
of electronic circuits and devices, electromagnetic fields, and
coupled problems. The methodological focus was on model order
reduction and uncertainty quantification.
This book discusses the state-of-the-art and open problems in
computational finance. It presents a collection of research
outcomes and reviews of the work from the STRIKE project, an FP7
Marie Curie Initial Training Network (ITN) project in which
academic partners trained early-stage researchers in close
cooperation with a broader range of associated partners, including
from the private sector. The aim of the project was to arrive at a
deeper understanding of complex (mostly nonlinear) financial models
and to develop effective and robust numerical schemes for solving
linear and nonlinear problems arising from the mathematical theory
of pricing financial derivatives and related financial products.
This was accomplished by means of financial modelling, mathematical
analysis and numerical simulations, optimal control techniques and
validation of models. In recent years the computational complexity
of mathematical models employed in financial mathematics has
witnessed tremendous growth. Advanced numerical techniques are now
essential to the majority of present-day applications in the
financial industry. Special attention is devoted to a uniform
methodology for both testing the latest achievements and
simultaneously educating young PhD students. Most of the
mathematical codes are linked into a novel computational finance
toolbox, which is provided in MATLAB and PYTHON with an open access
license. The book offers a valuable guide for researchers in
computational finance and related areas, e.g. energy markets, with
an interest in industrial mathematics.
This book discusses the state-of-the-art and open problems in
computational finance. It presents a collection of research
outcomes and reviews of the work from the STRIKE project, an FP7
Marie Curie Initial Training Network (ITN) project in which
academic partners trained early-stage researchers in close
cooperation with a broader range of associated partners, including
from the private sector. The aim of the project was to arrive at a
deeper understanding of complex (mostly nonlinear) financial models
and to develop effective and robust numerical schemes for solving
linear and nonlinear problems arising from the mathematical theory
of pricing financial derivatives and related financial products.
This was accomplished by means of financial modelling, mathematical
analysis and numerical simulations, optimal control techniques and
validation of models. In recent years the computational complexity
of mathematical models employed in financial mathematics has
witnessed tremendous growth. Advanced numerical techniques are now
essential to the majority of present-day applications in the
financial industry. Special attention is devoted to a uniform
methodology for both testing the latest achievements and
simultaneously educating young PhD students. Most of the
mathematical codes are linked into a novel computational finance
toolbox, which is provided in MATLAB and PYTHON with an open access
license. The book offers a valuable guide for researchers in
computational finance and related areas, e.g. energy markets, with
an interest in industrial mathematics.
This book is a collection of selected papers presented at the 10th
International Conference on Scientific Computing in Electrical
Engineering (SCEE), held in Wuppertal, Germany in 2014. The book is
divided into five parts, reflecting the main directions of SCEE
2014: 1. Device Modeling, Electric Circuits and Simulation, 2.
Computational Electromagnetics, 3. Coupled Problems, 4. Model Order
Reduction, and 5. Uncertainty Quantification. Each part starts with
a general introduction followed by the actual papers. The aim of
the SCEE 2014 conference was to bring together scientists from
academia and industry, mathematicians, electrical engineers,
computer scientists, and physicists, with the goal of fostering
intensive discussions on industrially relevant mathematical
problems, with an emphasis on the modeling and numerical simulation
of electronic circuits and devices, electromagnetic fields, and
coupled problems. The methodological focus was on model order
reduction and uncertainty quantification.
The fourth international conference on Scientific Computing in
Electrical En- gineering (SCEE) was held at the Eindhoven
University of Technology, from 23rd to 28th June, 2002. It was
sponsored by Philips Research Laborato- ries Eindhoven, the
Eindhoven University of Technology, Computer Simula- tion
Technology (CST) from Darmstadt, ABB Corporate Research, Thales
Netherlands,the European Consortium for Mathematics in Industry
(ECMI), the University of Rostock (organiser of SCEE-2000), the
European network for Mathematics, Computing and Simulation for
Industry (MACSI-net), the Royal Netherlands Academy of Arts and
Sciences (KNAW), and the Scien- tific Computing Group of the
Eindhoven University of Technology. The Program Committee consisted
of: Dr. Alain Bossavit, Electricite de France, Clamart, France. Dr.
Uwe Feldmann, Infineon Technologies A.G., Munich, Germany. Prof.Dr.
Leszek Demkowicz, University of Texas at Austin, USA. Dr. Michael
Gunther, Universitat Karlsruhe, Germany. Prof.Dr. Ulrich Langer,
Johannes Kepler Universitat, Linz, Austria. Dr. Jan ter
Maten,Philips Research Laboratories Eindhoven, The Nether- lands.
Prof.Dr. Ursula van Rienen, Universitat Rostock, Germany. Prof.Dr.
Jaijeet Roychowdhury, University of Minnesota, USA. - Prof.Dr. Wil
Schilders, Technische Universiteit Eindhoven and Philips Research
Laboratories Eindhoven, The Netherlands. - Prof.Dr. Thomas Weiland,
Technische Universitat Darmstadt, Germany.
Simulation based on mathematical models plays a major role in
computer aided design of integrated circuits (ICs). Decreasing
structure sizes, increasing packing densities and driving
frequencies require the use of refined mathematical models, and to
take into account secondary, parasitic effects. This leads to very
high dimensional problems which nowadays require simulation times
too large for the short time-to-market demands in industry. Modern
Model Order Reduction (MOR) techniques present a way out of this
dilemma in providing surrogate models which keep the main
characteristics of the device while requiring a significantly lower
simulation time than the full model. With Model Reduction for
Circuit Simulation we survey the state of the art in the
challenging research field of MOR for ICs, and also address its
future research directions. Special emphasis is taken on aspects
stemming from miniturisations to the nano scale. Contributions
cover complexity reduction using e.g., balanced truncation,
Krylov-techniques or POD approaches. For semiconductor applications
a focus is on generalising current techniques to
differential-algebraic equations, on including design parameters,
on preserving stability, and on including nonlinearity by means of
piecewise linearisations along solution trajectories (TPWL) and
interpolation techniques for nonlinear parts. Furthermore the
influence of interconnects and power grids on the physical
properties of the device is considered, and also top-down system
design approaches in which detailed block descriptions are combined
with behavioral models. Further topics consider MOR and the
combination of approaches from optimisation and statistics, and the
inclusion of PDE models with emphasis on MOR for the resulting
partial differential algebraic systems. The methods which currently
are being developed have also relevance in other application areas
such as mechanical multibody systems, and systems arising in
chemistry and to biology. The current number of books in the area
of MOR for ICs is very limited, so that this volume helps to fill a
gap in providing the state of the art material, and to stimulate
further research in this area of MOR. Model Reduction for Circuit
Simulation also reflects and documents the vivid interaction
between three active research projects in this area, namely the
EU-Marie Curie Action ToK project O-MOORE-NICE (members in Belgium,
The Netherlands and Germany), the EU-Marie Curie Action RTN-project
COMSON (members in The Netherlands, Italy, Germany, and Romania),
and the German federal project System reduction in nano-electronics
(SyreNe).
Simulation based on mathematical models plays a major role in
computer aided design of integrated circuits (ICs). Decreasing
structure sizes, increasing packing densities and driving
frequencies require the use of refined mathematical models, and to
take into account secondary, parasitic effects. This leads to very
high dimensional problems which nowadays require simulation times
too large for the short time-to-market demands in industry. Modern
Model Order Reduction (MOR) techniques present a way out of this
dilemma in providing surrogate models which keep the main
characteristics of the device while requiring a significantly lower
simulation time than the full model. With Model Reduction for
Circuit Simulation we survey the state of the art in the
challenging research field of MOR for ICs, and also address its
future research directions. Special emphasis is taken on aspects
stemming from miniturisations to the nano scale. Contributions
cover complexity reduction using e.g., balanced truncation,
Krylov-techniques or POD approaches. For semiconductor applications
a focus is on generalising current techniques to
differential-algebraic equations, on including design parameters,
on preserving stability, and on including nonlinearity by means of
piecewise linearisations along solution trajectories (TPWL) and
interpolation techniques for nonlinear parts. Furthermore the
influence of interconnects and power grids on the physical
properties of the device is considered, and also top-down system
design approaches in which detailed block descriptions are combined
with behavioral models. Further topics consider MOR and the
combination of approaches from optimisation and statistics, and the
inclusion of PDE models with emphasis on MOR for the resulting
partial differential algebraic systems. The methods which currently
are being developed have also relevance in other application areas
such as mechanical multibody systems, and systems arising in
chemistry and to biology. The current number of books in the area
of MOR for ICs is very limited, so that this volume helps to fill a
gap in providing the state of the art material, and to stimulate
further research in this area of MOR. Model Reduction for Circuit
Simulation also reflects and documents the vivid interaction
between three active research projects in this area, namely the
EU-Marie Curie Action ToK project O-MOORE-NICE (members in Belgium,
The Netherlands and Germany), the EU-Marie Curie Action RTN-project
COMSON (members in The Netherlands, Italy, Germany, and Romania),
and the German federal project System reduction in nano-electronics
(SyreNe).
The fourth international conference on Scientific Computing in
Electrical En- gineering (SCEE) was held at the Eindhoven
University of Technology, from 23rd to 28th June, 2002. It was
sponsored by Philips Research Laborato- ries Eindhoven, the
Eindhoven University of Technology, Computer Simula- tion
Technology (CST) from Darmstadt, ABB Corporate Research, Thales
Netherlands,the European Consortium for Mathematics in Industry
(ECMI), the University of Rostock (organiser of SCEE-2000), the
European network for Mathematics, Computing and Simulation for
Industry (MACSI-net), the Royal Netherlands Academy of Arts and
Sciences (KNAW), and the Scien- tific Computing Group of the
Eindhoven University of Technology. The Program Committee consisted
of: Dr. Alain Bossavit, Electricite de France, Clamart, France. Dr.
Uwe Feldmann, Infineon Technologies A.G., Munich, Germany. Prof.Dr.
Leszek Demkowicz, University of Texas at Austin, USA. Dr. Michael
Gunther, Universitat Karlsruhe, Germany. Prof.Dr. Ulrich Langer,
Johannes Kepler Universitat, Linz, Austria. Dr. Jan ter
Maten,Philips Research Laboratories Eindhoven, The Nether- lands.
Prof.Dr. Ursula van Rienen, Universitat Rostock, Germany. Prof.Dr.
Jaijeet Roychowdhury, University of Minnesota, USA. - Prof.Dr. Wil
Schilders, Technische Universiteit Eindhoven and Philips Research
Laboratories Eindhoven, The Netherlands. - Prof.Dr. Thomas Weiland,
Technische Universitat Darmstadt, Germany.
Designs in nanoelectronics often lead to challenging simulation
problems and include strong feedback couplings. Industry demands
provisions for variability in order to guarantee quality and yield.
It also requires the incorporation of higher abstraction levels to
allow for system simulation in order to shorten the design cycles,
while at the same time preserving accuracy. The methods developed
here promote a methodology for circuit-and-system-level modelling
and simulation based on best practice rules, which are used to deal
with coupled electromagnetic field-circuit-heat problems, as well
as coupled electro-thermal-stress problems that emerge in
nanoelectronic designs. This book covers: (1) advanced
monolithic/multirate/co-simulation techniques, which are combined
with envelope/wavelet approaches to create efficient and robust
simulation techniques for strongly coupled systems that exploit the
different dynamics of sub-systems within multiphysics problems, and
which allow designers to predict reliability and ageing; (2) new
generalized techniques in Uncertainty Quantification (UQ) for
coupled problems to include a variability capability such that
robust design and optimization, worst case analysis, and yield
estimation with tiny failure probabilities are possible (including
large deviations like 6-sigma); (3) enhanced sparse, parametric
Model Order Reduction techniques with a posteriori error estimation
for coupled problems and for UQ to reduce the complexity of the
sub-systems while ensuring that the operational and coupling
parameters can still be varied and that the reduced models offer
higher abstraction levels that can be efficiently simulated. All
the new algorithms produced were implemented, transferred and
tested by the EDA vendor MAGWEL. Validation was conducted on
industrial designs provided by end-users from the semiconductor
industry, who shared their feedback, contributed to the
measurements, and supplied both material data and process data. In
closing, a thorough comparison to measurements on real devices was
made in order to demonstrate the algorithms' industrial
applicability.
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