NA Digest, V. 21, # 44

NA Digest Wednesday, December 01, 2021 Volume 21 : Issue 44


Today's Editor:

Daniel M. Dunlavy
Sandia National Labs
dmdunla@sandia.gov

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From: Alex Pothen apothen@purdue.edu
Date: November 27, 2021
Subject: Bob Skeel, 1947-2021


We are saddened to announce that our colleague and friend Robert
D. Skeel, Professor of Computer Science (retired) at Purdue
University, passed away on November 1 in Phoenix AZ, after a battle
with cancer.

Bob was born in Calgary, Alberta in 1947, and grew up in Edmonton. He
received his PhD in Computing Science from the University of Alberta
in 1974, and then joined the University of Illinois at Urbana
Champaign as a faculty member. He rose through the ranks at Illinois,
becoming full Professor in 1986. After Bob retired from Illinois in
2004, he joined Purdue's Computer Science department, where he served
until retirement in 2017. Bob then moved to Phoenix AZ, continuing to
be active in research at Arizona State University.

Bob's research interests included computational molecular biophysics,
numerical ordinary differential equations, and linear algebra. In
2011, Bob was elected SIAM Fellow for his fundamental contributions to
these three areas. His most recent work was focused on computational
methods for biomolecular simulation. He was a major contributor to the
development of NAMD (a parallel molecular dynamics code designed for
high-performance simulation of large biomolecular systems) with the
principal investigator of an NIH project---the late Klaus Schulten, a
UIUC colleague of Bob. The developers of the NAMD software received
the 2002 Gordon Bell Award, the 2012 Sidney Fernbach Award, and the
2020 Gordon Bell Prize. With Jerry Keiper, Bob co-authored a textbook:
Elementary Numerical Computing with Mathematica.

Bob is survived by his wife Marjorie, two daughters, their spouses,
and four grandchildren. A private family memorial service was held on
November 5 at Christ Church Anglican in Phoenix AZ. Bob will be
greatly missed by his family, friends and colleagues. Bob's family
have provided an obituary available at
https://www.cs.purdue.edu/homes/apothen/BobSkeel-Obit.pdf. Friends
and colleagues could express their condolences to Bob's family through
messages sent to one of us at our email addresses.

Ahmed Sameh (sameh44@gmail.com) and Alex Pothen (apothen@purdue.edu)
Department of Computer Science, Purdue University


From: Sven Leyffer leyffer@anl.gov
Date: November 25, 2021
Subject: Call for Nominations, ICIAM Olga-Taussky-Todd Lecture


The International Council for Industrial and Applied Mathematics
(ICIAM) is inviting nominations for the 2023 Olga Taussky-Todd
Lecture, to be given at the ICIAM 2023 Congress.

A nomination consists of (1) the full name and address of the nominee,
(2) the web-page of the nominee, (3) a two-page justification for the
nomination, (4) 2-3 two-page letters of support, (5) the CV of the
nominee, and (6) the name and contact details of the nominator.

Nominations should be submitted at https://iciamprizes.org/ (the
deadline is December 30th, 2021). Please contact president@iciam.org
if you have any question regarding the nomination procedure.

More details of the award and the call for nominations can be found
here: https://iciam.org/iciam-olga-taussky-todd-lectures

Sven Leyffer
ICIAM Secretary
secretary@iciam.org
leyffer@anl.gov



From: Pamela Bye pam.bye@ima.org.uk
Date: November 26, 2021
Subject: Inverse Problems from Theory to Application, UK, May 2022


3- 5 May 2022
International Centre for Mathematical Sciences, Edinburgh
https://ima.org.uk/18111/3rd-ima-conference-on-inverse-problems-from-theory-to-application/

The aim of this conference is to bring together the applied
mathematics, statistics, machine learning, engineering, physics and
industrial communities around the topic of inverse problems to discuss
recent developments and open challenges in theory, methodology,
computational algorithms, and applications. We welcome industrial
representatives, doctoral students, early career and established
academics working in this field to attend. Topics of interest
include, for example, Inverse problems in mathematical and
computational imaging; Inverse problems in science, medicine,
engineering, and other fields; Model-based and data-driven methods for
solving inverse; Optimisation, statistical, and machine learning
methods for solving inverse problems; Mathematical theory for inverse
problems; Deterministic and stochastic computational methods and
algorithms.

Papers will be accepted for the conference based on a 100 word
abstract for oral or poster presentation. We welcome abstracts to be
submitted by 7th January 2022 via https://my.ima.org.uk./. Please
indicate whether your title is intended for oral "presentation" or
"poster" presentation. Please send your abstracts in plain text format
(no equations).

Registration:
Registration is now open via: https://my.ima.org.uk./

Confirmed Invited Speakers: Thomas Pock (Graz University of
Technology, Austria), Gabriele Steidl (Berlin Institute of Technology,
Germany), Jason McEwen (University College London & Kagenova), Yi Yu
(University of Warwick, UK), Andrew Duncan (Imperial College London,
UK), Luca Calatroni (CNRS & Nice University, France).

For further information on this conference, please visit the
conference webpage:
https://ima.org.uk/18111/3rd-ima-conference-on-inverse-problems-from-theory-to-application/



From: Carola Schonlieb cbs31@cam.ac.uk
Date: November 30, 2021
Subject: Research Associate Position, ML for Brain and Mental Health, UK


Applications are invited for a Research Associate position in Machine
Learning for Brain and Mental Health to work with Prof Carola-Bibiane
Schonlieb in the Cambridge Image Analysis Group
(http://www.damtp.cam.ac.uk/research/cia/), Department of Applied
Mathematics and Theoretical Physics, and Prof Zoe Kourtzi at the
Adaptive Brain Lab (http://www.abg.psychol.cam.ac.uk) University of
Cambridge.

The position will focus on the development and implementation of
state-of- the-art machine learning and image analysis techniques for
the early diagnosis of mental health disorders (e.g. dementia,
mood-related disorders). The research aims to develop
biologically-inspired artificial systems for precision brain and
mental health by bringing together expertise in machine learning, data
science, neuroscience, and clinical practice.

The successful candidate will have or about to be awarded a PhD in a
relevant area (e.g. Mathematics, Computer Science, Engineering,
Biostatistics, Neuroscience, Medicine), together with a strong
academic track record. Programming skills are highly desirable and
experience with machine learning, data science, medical image
analysis, biostatistics, or clinical neuroscience/neurology are highly
beneficial. Above all, they will demonstrate enthusiasm to contribute
new knowledge, openness to learn new approaches and willingness to
contribute to a multidisciplinary team across academia and industry.

Application deadline: 3 January 2022.

For more details on the position and on how to apply see
https://www.jobs.cam.ac.uk/job/32357/

For informal enquiries please just email me cbs31@cam.ac.uk


From: Silvana ILIE silvana@ryerson.ca
Date: November 30, 2021
Subject: Postdoc Position, Computational Biology, Ryerson Univ, Canada


A postdoctoral position is available in Computational Biology in the
Department of Mathematics, Ryerson University
(http://www.math.ryerson.ca). The research will be led jointly by Dr.
Silvana Ilie and Dr. Katrin Rohlf. This position provides an
opportunity to engage in research in Applied Mathematics, with a
limited amount of teaching. The salary is competitive, with funding
provided for one year.

We are seeking qualified and motivated applicants in Applied
Mathematics, to work on interdisciplinary projects aimed at developing
stochastic modelling and simulation tools for studying biological
systems. The ideal candidate would have a strong background in
Applied Mathematics (Numerical Analysis and Probability) and/or
Computer Science. Strong programming skills in Matlab are
mandatory. In addition, experience with dynamical systems (ODEs and
PDEs) is expected. Knowledge of biological/chemical reaction
modelling, stochastic simulation (temporal and spatio-temporal) and
machine learning would be considered an asset.

The starting date of the position is flexible, but no later than April
1, 2022. The review of applications will begin immediately, and will
continue until the position is filled. Applicants should submit a
curriculum vitae, a one page summary of research experience, and a
minimum of two letters of recommendation. At least one of these
letters should report on the candidate's teaching
abilities. Application material and reference letters should be sent
directly by e-mail to compbio@ryerson.ca


From: Idoia Hernandez recruitment@bcamath.org
Date: November 26, 2021
Subject: Postdoc Position, Inverse Methods, BCAM


Topisc: Deep Learning, Data-Driven Computing, Partial Differential
Equations, Inverse Problems, Offshore Wind Energy

Deadline: December 10, 2021
Applications must be submitted on-line at:
http://www.bcamath.org/en/research/job/ic2021-11-postdoctoral-fellow-on-inverse-methods-for-structural-health-monitoring-of-offshore-wind-energy-technologies

Applicants must have their PhD completed before the contract starts.
PhD in Mathematics and/or Civil, Mechanical, Industrial, Offshore
Engineering or similar areas

Skills: Good interpersonal skills. A proven track record in quality
research, as evidenced by research publications in top scientific
journals and conferences. Demonstrated ability to work independently
and as part of a collaborative research team. Ability to present and
publish research outcomes in spoken (talks) and written (papers) form.
Ability to effectively communicate and present research ideas to
researchers and stakeholders with different backgrounds. Fluency in
spoken and written English.

The preferred candidate will have: Strong background in the numerical
solution of Partial Differential Equations and/or Deep Learning
techniques. Background in Inverse Problems. Experience in treatment
of long time series. Experience in simulation of long time series.
Experience in modelling Failure Modes and Effects Analysis (FMEA) for
components / subsystems / systems. Good programming skills in Python
and preferably, also Tensorflow. Interest and disposition to work in
interdisciplinary groups. The candidate would preferably be in
possess of: Experience in the sector of offshore wind energy, or
offshore oil and gas, or structures in offshore environment.


From: Idoia Hernandez recruitment@bcamath.org
Date: November 26, 2021
Subject: Postdoc Position, Machine Learning for Energy Forecasting, BCAM


Topic: Machine Learning for energy forecasting
Deadline for application: December 10, 2021
Applications must be submitted on-line at:
http://www.bcamath.org/en/research/job/ic2021-11-postdoctoral-fellow-on-machine-learning-for-energy-forecasting

Requitements: Applicants must have their PhD completed before the
contract starts.

Skills: Good interpersonal skills. A proven track record in quality
research, as evidenced by research publications in top scientific
journals and conferences. Demonstrated ability to work independently
and as part of a collaborative research team. Ability to present and
publish research outcomes in spoken (talks) and written (papers) form.
Ability to effectively communicate and present research ideas to
researchers and stakeholders with different backgrounds. Fluency in
spoken and written Spanish and English.

The preferred candidate will have: Strong background in
mathematics/statistics. Strong background in data science/machine
learning. Background in time series prediction. Background in
energy-related applications such as load forecasting. Good
programming skills in Python and/or Matlab. Interest and disposition
to work in interdisciplinary groups and collaborate with multiple
companies/institutions.


From: Susanne Brenner sbrenner@cct.lsu.edu
Date: November 30, 2021
Subject: Postdoc Position, Numerical Analysis, Louisiana State Univ


The Center for Computation & Technology (CCT) at Louisiana State
University (LSU) is seeking applications to fill a postdoctoral
researcher position starting August 2022. The postdoctoral researcher
will work under the supervision of Professor Susanne C. Brenner.

Preferred Qualifications: Research excellence. Ideal candidates will
be published research in at least one of the following areas: adaptive
finite element methods, discontinuous Galerkin methods, multigrid
methods, domain decomposition methods, elliptic optimal control
problems, computational electromagnetics or computational mechanics.

Quick link to ad URL:
https://lsu.wd1.myworkdayjobs.com/LSU/job/1079-Digital-Media-Center/Postdoctoral-Researcher_R00062258

Please arrange for at least three reference letters to be sent
directly to sbrenner@cct.lsu.edu

Deadline to apply: January 15, 2022.


From: Per-Gunnar Martinsson pgm@oden.utexas.edu
Date: November 29, 2021
Subject: Postdoc Position, Randomized Numerical LA, UT-Austin


Applications are invited for a postdoctoral research position in the
Oden Institute for Computational Engineering and Sciences at The
University of Texas at Austin, in the research group of Gunnar
Martinsson. The targeted research area is randomized algorithms for
solving linear systems, for low rank approximation, and for related
problems in linear algebra, optimization, and scientific computing.

Review of applications will start December 15, but the position will
remain open until filled. The starting date is flexible, but no later
than September 2022 would be preferred.

The initial appointment is for one year, with the possibility of
renewal based upon availability of funding and performance. The
appointment can be renewed up to 4 times, but an overall duration of 2
or 3 years would normally be expected.

For additional details on required qualifications, and how to apply, see:
https://utaustin.wd1.myworkdayjobs.com/en-US/UTstaff/details/Postdoctoral-Fellow_R_00016355
https://users.oden.utexas.edu/~pgm/main_positions.html


From: Idoia Hernandez recruitment@bcamath.org
Date: November 26, 2021
Subject: Postdoc Position, Special Functions and Random Walks, BCAM


Inside the Generalized Master Equation for the Continuous-Time Random
Walk. The proposed research project is focused on the derivation of
the Generalized Master Equation (GME) for the Continuous-Time Random
Walk (CTRW) as published in literature, e.g., [1,2], and on its
specific determination for fractional diffusion [3]. Actually, the GME
depends on a kernel function that is explicitly given in terms of the
jumps and waiting-times distributions of the CTRW. Surprisingly, a
systematic study concerning the features of the CTRW, the kernel of
the GME and the resulting walker's distribution is not provided,
yet. The aim of the research is to fill this literature gap in the
view of the many applications of the CTRW and in particular because of
the recent regime-transitions (exponential-to-fractional-to-Gaussian)
observed in anomalous diffusion processes.
[1] Klafter J and Silbey R 1980 Phys. Rev. Lett. 44 55-58
[2] Klafter J, Blumen A and Shlesinger M F 1987 Phys. Rev. A 35 3081-3085
[3] Hilfer R and Anton L 1995 Phys. Rev. E 51 R848-R851

Deadline: January 17, 2022
Applications must be submitted on-line at:
http://www.bcamath.org/en/research/job/ic2021-11-postdoctoral-fellowship-in-special-functions-and-random-walks
Requirements: Applicants must have their PhD completed before the
contract starts

Skills: Good interpersonal skills. A proven track record in quality
research, as evidenced by research publications in top scientific
journals and conferences. Demonstrated ability to work independently
and as part of a collaborative research team. Ability to present and
publish research outcomes in spoken (talks) and written (papers) form.
Ability to effectively communicate and present research ideas to
researchers and stakeholders with different backgrounds. Fluency in
spoken and written English.

The preferred candidate will have: Strong background in special
functions and integral transforms. Background in fractional calculus
and fractional modelling. Knowledge in statistics and probability.
Good programming skills in Mathematica and/or Maple and/or MathLab.
Interest and disposition to work in interdisciplinary groups.


From: Coralia Cartis cartis@maths.ox.ac.uk
Date: November 30, 2021
Subject: Postdoc Positions, Mathematical Foundations of Data, Oxford


Postdoctoral Research Associate in the Mathematical Foundations of
Data Science (3 posts), Mathematical Institute, University of Oxford

We invite applications for up to three Postdoctoral Research
Associates to work on an exciting new project to address significant
mathematical and engineering challenges arising from multidimensional
big data.

The positions are based at the Mathematical Institute, and form a key
part of a substantial new Centre for Intelligent Multidimensional Data
Analysis (CIMDA) funded by the Hong Kong Innovation and Technology
Commission. Each position is available full time for 12 months in the
first instance, though funding for the project is expected to be
renewed and if so, it may be possible to extend the length of this
appointment by another year. They are available for an immediate
start.

The prospective researchers will work on projects potentially related
to scalable optimization algorithm development and analysis,
stochastic processes and rough paths, reduced precision and
parallel-in-time computations, high-dimensional matrices and tensors
and their decompositions, hypergraphs models, learning of
non-Euclidean data, sparse deep neural networks, and more; under the
supervision of a subteam selected from Profs Terry Lyons, Coralia
Cartis, Mike Giles, Raphael Hauser, Harald Oberhauser, Endre Suli,
Jared Tanner and Andrew Wathen. We welcome applications from
candidates from all nationalities and backgrounds.

Please follow this link for more information and to apply:
https://my.corehr.com/pls/uoxrecruit/erq_jobspec_details_form.jobspec?p_id=3D154459

CLOSING DATE: December 8th, 2021.


From: Ron Boisvert boisvert@nist.gov
Date: November 30, 2021
Subject: Postdoc Positions, NIST


The Applied and Computational Mathematics Division (ACMD) of the
National Institute of Standards and Technology (NIST) invites
applications for two-year NRC postdoctoral research positions at NIST
Laboratories in Gaithersburg, Maryland, and Boulder, Colorado. NIST is
a Federal government research laboratory specializing in measurement
science. ACMD consists of some 50 full-time professional staff, along
with part-time faculty appointees and guest researchers. Staff members
engage in collaborative research with scientists throughout NIST,
providing expertise in applied mathematics and computational science
and engineering. Research areas of interest include computational
materials science, computational electromagnetics, computational
biology, computational chemistry, orthogonal polynomials and special
functions, applied optimization and simulation, complex systems and
networks, data mining, scientific visualization, parallel and
distributed algorithms, and quantum information science.

Candidates and their research proposals are evaluated in a competitive
process managed by the National Research Council (NRC) Associateship
Programs. The current stipend is $72,750 per year. For further
details, see http://www.nist.gov/itl/math/mcsd-postdoctoral-
opportunities.cfm. Application deadlines are February 1 and August 1.
Appointments commence within one year of selection. For questions,
contact Ron Boisvert, boisvert@nist.gov.

NIST is an equal opportunity/affirmative action employer. The NRC
Associateship Program at NIST is restricted to US citizens.


From: Gunther Reissig gunther2016@reiszig.de
Date: November 30, 2021
Subject: Postdoc/PhD Positions, Formal Methods, Germany


We invite applications for two post-doctoral researcher positions in
the field of formal methods, in Munich, Germany, the city of the
Oktoberfest. The successful candidates are expected to advance the
state of the art of abstraction-based synthesis and verification, to
facilitate routine and efficient application of the
approach. Depending on background and interests of each candidate, the
research focus will be either on theoretical foundations, or on
algorithms and software development.

Required qualifications: PhD degree in Mathematics, Systems and
Control, Computer Science, or a related field. Exceptionally qualified
and experienced candidates with an MSc degree will also be considered.
Strong theoretical or mathematical background, and a strong interest
in dynamical or control systems. In addition, experience in one of the
following fields is required: Optimal control; semi-definite
programming; quadrature theory; set-valued numerics; reachability
analysis; validated floating-point arithmetic; compiler design;
software development. Programming proficiency (C or Ada or
Mathematica). Efficient communication skills in English.

The position is full-time and paid according to pay scale ``TVOeD
Bund, E 14''. Actual income depends on marital status and professional
experience, and starts from EUR 35000 net p.a. (E-13/EUR 32700 for
applicants with an MSc degree). Reimbursement for travel expenses to
conferences. No teaching load. The positions are available immediately
and for a duration until December 2023, with possible extension
contingent on research performance. They are open to applicants
worldwide; no special security clearance necessary.

Your complete application consists of the following documents, which
should be sent as a single PDF file to the email address given below
(deadline: December 22, 2021): CV; One-page cover letter (clearly
indicating available start date as well as relevant qualifications,
experience and motivation); University certificates and transcripts
(BSc, MSc and PhD degrees); Up to three letters of recommendation;
List of publications; Possibly an English language certificate.

All documents should be in English or German.


From: Idoia Hernandez recruitment@bcamath.org
Date: November 26, 2021
Subject: Research Technician Position, Computational Methods, BCAM


Topic: Machine Learning, Data-Driven Computing, Numerical Simulation,
Degradation Models, Transfer Learning, Deep Learning, Partial
Differential Equations, Inverse Problems

Deadline: December 10, 2021

Applications must be submitted on-line at:
http://www.bcamath.org/en/research/job/ic2021-11-research-technician-on-computational-methods-for-reliability-and-structural-health-monitoring

Requirements: Applicants must have their Bachelor's or Master degree
preferable in Physics, Mathematics, Civil/Mechanical/industrial
Engineering, or related fields Possess of PhD in the fields will be
positively considered in the evaluation

Skills: Good interpersonal skills. A proven track record in quality
research, as evidenced by research publications in top scientific
journals and conferences. Demonstrated ability to work independently
and as part of a collaborative research team. Ability to present and
publish research outcomes in spoken (talks) and written (papers) form.
Ability to effectively communicate and present research ideas to
researchers and stakeholders with different backgrounds. Fluency in
spoken and written English.

The preferred candidate will have: Background in inverse problems.
Background in reliability modelling applied to structural mechanics.
Experience in treatment and simulation of time series. Good
programming skills in Python and R. Interest and disposition to work
in interdisciplinary groups.


From: Idoia Hernandez recruitment@bcamath.org
Date: November 26, 2021
Subject: Research Technician Position, Numerical Techniques/CFD, BCAM


Topics: Computational Fluid Dynamics, Reduced Order Methods,
Surrogated Models, Finite Elements, Finite Volumes

Deadline: December 10, 2021
Applications must be submitted on-line at:
http://www.bcamath.org/en/research/job/ic2021-11-research-technician-on-alternative-numerical-techniques-for-computational-fluid-dynamics

Requirements: Applicants must have their Bachelor's or Master degree
preferable in Physics, Mathematics, Civil/Mechanical/industrial
Engineering, or related fields Possess of PhD in the fields will be
positively considered in the evaluation

Skills: Good interpersonal skills. A proven track record in quality
research, as evidenced by research publications in top scientific
journals and conferences. Demonstrated ability to work independently
and as part of a collaborative research team. Ability to present and
publish research outcomes in spoken (talks) and written (papers) form.
Ability to effectively communicate and present research ideas to
researchers and stakeholders with different backgrounds. Fluency in
spoken and written English.

The preferred candidate will have: Background in Computational Fluid
Dynamics. Background in Finite Element and Finite Volume methods.
Background in Reduced Order Models (ROMs) and surrogated models. Good
programming skills (bash, C++, Python &) Knowledge of Linux OS.
Interest and disposition to work in interdisciplinary groups.

The candidate would preferably be in possess of: Experience of using
OpenFOAM.


From: Yonghui Yu yyu@lsec.cc.ac.cn
Date: November 29, 2021
Subject: Contents, Computational Mathematics, 39 (6)


Journal of Computational Mathematics, Volume 39 (2021), issue 6

CONTENTS

Deep ReLU Networks Overcome the Curse of Dimensionality for
Generalized Bandlimited Functions, Hadrien Montanelli, Haizhao Yang
and Qiang Du

Physics Informed Neural Networks (PINNs) for Approximating Nonlinear
Dispersive PDEs, Genming Bai, Ujjwal Koley, Siddhartha Mishra and
Roberto Molinaro

An Acceleration Strategy for Randomize-then-optimize Sampling via Deep
Neural Networks, Liang Yan and Tao Zhou

Convergence of the Weighted Nonlocal Laplacian on Random Point Cloud,
Zuoqiang Shi and Bao Wang

Achieving Adversarial Robustness Requires an Active Teacher, Chao Ma
and Lexing Ying

The Random Batch Method for N-Body Quantum Dynamics, Francois Golse,
Shi Jin and Thierry Paul


From: Raiondas Ciegis rc@vgtu.lt
Date: November 29, 2021
Subject: Contents, Mathematical Modelling and Analysis, 26 (4)


MATHEMATICAL MODELLING AND ANALYSIS: The Baltic Journal on
Mathematical Applications, Numerical Analysis and Differential
Equations, ISSN 1392-6292, ISSN 1648-3510 online, Electronical
edition: http://mma.vgtu.lt

Raimondas Ciegis (Editor) Volume 26, Issue 4, 2021

CONTENTS

Jishan Fan, Peng Wang and Yong Zhou, Uniform Regularity for the
Isentropic Compressible Magneto-Micropolar System

Xiaoxia Dai and Chengwei Zhang, A Subgrid Stabilized Method for
Navier-Stokes Equations with Nonlinear Slip Boundary Conditions

Feliz Minhs and Rui Carapinha, Third-Order Generalized Discontinuous
Impulsive Problems on the Half-Line

Kassimu Mpungu and Tijani A. Apalara, Exponential Stability of
Laminated Beam with Constant Delay Feedback

Imre Ferenc Barna and Laszlo Matyas, Analytic Solutions of a Two-Fluid
Hydrodynamic Model

Emile Franc Doungmo Goufo, Chokkalingam Ravichandran and Gunvant
A. Birajdar, Self-Similarity Techniques for Chaotic Attractors with
Many Scrolls Using Step Series Switching

Armands Gritsans and Inara Yermachenko, On the Maximum Number of
Period Annuli for Second Order Conservative Equations

Milan Medved and Eva Brestovanska, Differential Equations with
Tempered $\mathbf{\Psi-}$Caputo Fractional Derivative

Konstantinas Pileckas and Alicija Raciene, On Singular Solutions of
the Stationary Navier-Stokes System in Power Cusp Domains

Farah Balaadich and Elhoussine Azroul, An Existence Result for
Quasilinear Parabolic Systems with Lower Order Terms

Joel Chaskalovic and Franck Assous, Numerical Validation of
Probabilistic Laws to Evaluate Finite Element Error Estimates


End of Digest
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