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Subject: NA Digest, V. 20, # 43

NA Digest  Monday, November 09, 2020  Volume 20 : Issue 43

Today's Editor:

        Daniel M. Dunlavy
        Sandia National Labs
        [log in to unmask]

Today's Topics:

        New Book, Simplicial Partitions with Applications to the FEM
        AI and IoT for Flow Modeling, ONLINE, Nov 2020
        Senior-Level Position, Mathematical Analysis, McMaster Univ
        Postdoc Position, Computational Fluid Mechanics, Brazil
        Postdoc Position, Numerical Optimization, Argonne NL
        Postdoc Position, TU Berlin, Germany
        Postdoc Position, Theoretical Fluid Mechanics, McMaster
        Postdoc Positions, Applied Mathematics, Columbia Univ
        Postdoc Positions, Comp Mechanics, Czech Technical Univ
        Special Issue on Domain Decomposition Methods
        Special Issue, NA with Applications in Machine Learning

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Submissions for NA Digest:
        http://icl.utk.edu/na-digest/

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From: Sergey Korotov [log in to unmask]
Date: November 05, 2020
Subject: New Book, Simplicial Partitions with Applications to the FEM

Simplicial Partitions with Applications to the Finite Element Method
(Springer Monographs in Mathematics) 1st ed. 2020 Edition
by Jan Brandts, Sergey Korotov, Michal Krizek.

This monograph focuses on the mathematical and numerical analysis of
simplicial partitions and the finite element method. This active area
of research has become an essential part of physics and engineering,
for example in the study of problems involving heat conduction, linear
elasticity, semiconductors, Maxwell's equations, Einstein's equations
and magnetic and gravitational fields.  These problems require the
simulation of various phenomena and physical fields over complicated
structures in three (and higher) dimensions. Since not all structures
can be decomposed into simpler objects like d- dimensional rectangular
blocks, simplicial partitions are important. In this book an emphasis
is placed on angle conditions guaranteeing the convergence of the
finite element method for elliptic PDEs with given boundary
conditions.  It is aimed at a general mathematical audience who is
assumed to be familiar with only a few basic results from linear
algebra, geometry, and mathematical and numerical analysis.

The link to the table of contents is at:
https://www.springer.com/gp/book/9783030556761

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From: Kees Oosterlee [log in to unmask]
Date: November 09, 2020
Subject: AI and IoT for Flow Modeling, ONLINE, Nov 2020

AI and IoT for Flow Modeling
Friday 20th November 2020

The workshop is organized as part of the Indo-Dutch project, "Digital
Twins for pipeline transport networks".  The aim of the project is to
develop a digital twin that connects sensor data and advanced fluid
solvers in order to detect possible leakage of fluid from the pipeline
in real-time. Of particular interest then is also the development of
AI based fluid flow solvers, as traditional fluid flow models are
typically much too slow for real-time applications. We thank the NWO
(the Netherlands), MeiTY (India) and Shell (the Netherlands) for
funding the project. As part of the workshop the following talks have
been scheduled:

- 11:00 Jan S. Hesthaven (EPFL): Digital Twins at the interface
  between modeling, measurements, and machine learning
- 11:45 Yogesh Simhan, (IISc Bangalore): IoT and Analytics for Social
  Good
- 12:30 Vineet Tyagi (IIT Bombay): Neural Networks for predicting flow
  parameters in a pipeline network
- 13:00 Amritendu Mukherjee (IISc Bangalore): A comprehensive study to
  understand the relationship of urbanization and population density
  with GRACE TWS for selected study regions in India during 2003-2017
- 13:30 Nikolaj T. Mucke (CWI): Reduced Order Modeling for Fluid
  Simulations using Deep Learning
- 14:00 Ruud Henkes (Shell): The role of simulation in leak detection
  for pipeline operations

This workshop will take place, on-line, via zoom.us.
For the log-in details see
https://www.cwi.nl/research/groups/scientific-computing/events/workshop-20-
november-2020/ai-and-iot-for-flow-modeling

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From: Bartosz Protas [log in to unmask]
Date: November 05, 2020
Subject: Senior-Level Position, Mathematical Analysis, McMaster Univ

There is a senior-level opening in the broad area of mathematical
analysis and applications (which also includes numerical analysis) in
the Department of Mathematics and Statistics at McMaster
University. More information about this position is available at the
following webpage

https://www.mathjobs.org/jobs/application/16437

The expected start date for this position is July 1, 2021, and the
application deadline is December 1, 2020.


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From: João Luiz F. Azevedo [log in to unmask]
Date: November 05, 2020
Subject: Postdoc Position, Computational Fluid Mechanics, Brazil

Instituto de Aeronautica e Espaco (IAE), at the city of Sao Jose dos
Campos, Sao Paulo, Brazil, welcomes applications for a post-doc
fellowship in the project "Aerothermodynamic Analysis of Hypersonic
Flows with Applications in Atmospheric Reentry Procedure". The
fellowship is funded by the Fundacao de Amparo a Pesquisa do Estado de
Sao Paulo (FAPESP). The accepted candidate will receive a monthly
stipend of R$ 7.373,10 for 12 months, with possibility of extension
for another 12 months. The post-docs are allowed a grant, in the
amount of 15% of the annual fellowship, to finance items related to
the research activities.  The present project concerns numerical
simulations of hypersonic reactive flows in thermal
non-equilibrium. Main objectives of the work to be performed are
twofold: to address the effects of bulk viscosity in hypersonic
simulations, particularly for Mars entry-type flows, and to develop
the capability to simulate hypersonic flows involving coupled
radiative and convective heat transfer mechanisms. The position is
initially offered for 1 year with possible reappointment. The
researcher will work under the supervision of Prof. Azevedo at IAE and
ITA, in Sao Jose dos Campos, Brazil. Applicants must have received a
doctorate in Applied Mathematics, Mechanical or Aerospace Engineering,
or related discipline, within the last 5 years. Selection criteria
require demonstrated research ability in CFD, with a strong background
in the physical, mathematical and computational aspects of numerical
simulation of hypersonic flows, commitment to collaborative research,
and excellent verbal and written skills. To apply, please e-mail (only
PDF files) the following items to [log in to unmask] until 4
Dec.  2020: 1. Letter of interest, with full contact information and
citizenship/immigration status, concisely addressing the research
themes above; 2. Full CV ; 3. Cover letter with names/contact
information for 3 references (no letters please). Indicate "CEPID
Postdoc" in the subject field.


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From: Jeffrey Larson [log in to unmask]
Date: November 04, 2020
Subject: Postdoc Position, Numerical Optimization, Argonne NL

The Mathematics and Computer Science Division at Argonne National
Laboratory is seeking postdoctoral candidates with expertise in
numerical optimization to help solve important quantum information
science problems. Applicants with interest in theory, algorithms,
and/or software are encouraged to apply.

We especially encourage applications from researchers with interests
in stochastic optimization, optimal control, derivative-free
optimization, or combinatorial optimization.

U.S. citizenship is not required.

Candidates must have a recent doctoral degree in applied mathematics,
statistics, computer science, industrial/systems engineering, or
related field.  Expertise in one or more areas of numerical
optimization is also required.

This position is available immediately, but there is flexibility in
start dates.

Applications received by December 15, 2020 will receive full
consideration.  Questions can be addressed to Jeffrey Larson
([log in to unmask]) or Sven Leyffer ([log in to unmask]).

For more information, please see:
https://bit.ly/3jW6dKc

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From: Tobias Breiten [log in to unmask]
Date: November 05, 2020
Subject: Postdoc Position, TU Berlin, Germany

The Technical University of Berlin, Institute of Mathematics, invites
applications for a Postdoc (m-f-d) for a period of max. 2 years. The
research is part of the project "Optimal control of stochastic
modified equations for the efficient parametrisation of deep neural
networks" within the framework of the Cluster of Excellence MATH+, in
collaboration with the BTU Cottbus-Senftenberg (Prof. Dr. Carsten
Hartmann).

Requirements are successfully completed university degree (Master,
Diploma or equivalent) as well as PhD in mathematics or related area;
detailed knowledge in the areas of control theory, optimization with
stochastic and/or partial differential equations as well as numerical
methods; preliminary work on Fokker-Planck or Langevin equations is
desirable; good command of German and/or English, both written and
spoken, willingness to acquire lacking language skills.

Application deadline: November 20th, 2020.

For the full advertisement, please go to
https://stellenticket.de/84926/?lang=en

For further information, please contact Tobias Breiten, tobias.breiten
(at) tu-berlin.de

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From: Bartosz Protas [log in to unmask]
Date: November 04, 2020
Subject: Postdoc Position, Theoretical Fluid Mechanics, McMaster

An opening for a post-doctoral fellow is anticipated in Dr. Protas'
research group at McMaster University with a start date of September
1, 2021. The focus of this position will be fundamental investigations
of extreme behavior, such as potential singularity formation, in fluid
flow models using a combination of mathematical analysis and
large-scale computations. Expected background involves (ideally, a
combination of) theoretical fluid mechanics, large-scale scientific
computing (including numerical optimization) and PDE analysis. The
position will also involve a limited amount of teaching.  Applications
should be submitted via www.mathjobs.org (Fellowship ID: McMaster-PDF
[#16611]; the advertisement on www.mathjobs.org contains further
details concerning the opening and the application procedure).

Interested candidates may also contact

  Dr. Bartosz Protas
  Department of Mathematics & Statistics
  McMaster University
  Hamilton, Ontario, CANADA L8S 4K1
  Email: [log in to unmask]
  URL:   http://www.math.mcmaster.ca/bprotas

for additional information.

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From: Kui Ren [log in to unmask]
Date: November 03, 2020
Subject: Postdoc Positions, Applied Mathematics, Columbia Univ

The Program in Applied Mathematics
(https://appliedmath.apam.columbia.edu) at Columbia University invites
applications for two postdoctoral positions in applied mathematics
(broadly defined) starting Fall 2021. The positions are partially
supported by an Applied Mathematics Research Training Group (RTG)
grant from the National Science Foundation. The postdoctoral positions
are appointed annually and can be renewed up to a total of three
years. The appointments include teaching duties with an expected
teaching load of one course per semester.

Applications should be submitted through mathjobs.org at:
https://www.mathjobs.org/jobs/list/16622

For full consideration applicants are encouraged to submit all
materials before December 1, 2020.

Following the rules of the National Science Foundation, the
postdoctoral positions are restricted to US citizens, nationals, and
permanent residents.

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From: Jan Zeman [log in to unmask]
Date: November 05, 2020
Subject: Postdoc Positions, Comp Mechanics, Czech Technical Univ

Two fully-funded postdoctoral positions in computational mechanics of
materials and structures are available at the Department of Mechanics,
Faculty of Civil Engineering, Czech Technical University in Prague.

Please consult https://euraxess.ec.europa.eu/jobs/573988 for full
opening details. The application deadline is 5 December 2020.

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From: Victorita Dolean [log in to unmask]
Date: November 03, 2020
Subject: Special Issue on Domain Decomposition Methods

We would like to draw your attention to a special issue on Domain
Decomposition Methods
https://www.mdpi.com/journal/mca/special_issues/DDM

Mathematical modelling in science and engineering problems relies
heavily on partial differential equations. Accurate discretization of
such PDEs is very often required, and this usually leads to
potentially very large linear systems that must be solved in
parallel. The computational resources (in terms of hardware) and
computational time available can limit the high- fidelity of these
simulations. With the advent of parallel computers and the
availability of large computational clusters, algorithmic improvements
are key in reducing the computational time and increasing the model
complexity and accuracy. One of the success stories of parallel
computing is linear solvers, but also hybrid solvers, like domain
decomposition methods.

Contributions related to the development and analysis of the domain
decomposition solvers with their different aspects (linear or
non-linear, multilevel methods, scalability, HPC implementation,
coupling of mathematical models, and computational challenges of
large-scale problems) are welcome in this Special Issue.

Authors are also invited to submit any other relevant complementary
materials, such as software or available links illustrating their
research.


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From: Panagiota Tsompanopoulou [log in to unmask]
Date: November 03, 2020
Subject: Special Issue, NA with Applications in Machine Learning

We would like to draw your attention to a special issue on Numerical
Analysis with Applications in Machine Learning:
https://www.mdpi.com/journal/mathematics/special_issues/Nume_Analy_MacLear

The collection of large amounts of data produced by an enormous
variety of users has been a fact for many years now. Therefore, there
is a tremendous need to study, analyze, and process these data in
order to clear out the possible noise and derive the substantial
information. The methods used for such problems constitute the
scientific area of machine learning. Looking closer to the theory that
supports these methods, one recognizes many fields of numerical
analysis, such as Euclidean spaces with metrics and norms,
approximation theory, optimization theory, theory of matrices,
etc. The use of existing methods and the tuning of their parameters
still gives very interesting results for the treated problems.
Wishing to go further in the solution of already met or new and more
difficult problems, scientists have to go back to the roots of the
mathematics used in machine learning, to study, research and create
new, stable, and accurate methods.

Through this Special Issue, we invite our colleagues to submit
articles that rely on numerical analysis methods to address problems
in the field of machine learning, presenting both theoretical and
experimental results.  The fields of interest originate from
mathematics and computer science, including (but not limited to)
numerical linear algebra, Euclidean, pseudo- Euclidean and metric
spaces, theory of matrices, theory of approximation and optimization,
machine learning, computer vision, classification, clustering, and
pattern recognition.

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End of Digest
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