Subject: NA Digest, V. 21, # 45 NA Digest Sunday, December 05, 2021 Volume 21 : Issue 45 Today's Editor: Daniel M. Dunlavy Sandia National Labs [log in to unmask] Today's Topics: DFO-TR, Derivative-free Optimization Solver New Book, Modern Nonconvex Nondifferentiable Optimization New Book, Unification of Fractional Calculi with Applications Numerical Methods for Hyperbolic PDE Course, ONLINE, Jan 2022 Extreme Heterogeneity Solutions, South Korea, April 2022 Mathematics of Finance, UK, Jun 2022 Assistant Professor Position, ORFE, Princeton Univ Postdoc Position, Data Assimilation, Univ of Reading Postdoc Position, ML/Signal Processing, Claremont Graduate Univ Postdoc Position, Sandia Labs/CA, USA Postdoc Positions, Physical Simulation + Learning, Vanderbilt Univ PhD Positions, Computational Math/Sci/Eng, Michigan State Univ Subscribe, unsubscribe, change address, or for na-digest archives: http://www.netlib.org/na-digest-html/faq.html Submissions for NA Digest: http://icl.utk.edu/na-digest/ ------------------------------------------------------- From: Luis Nunes Vicente [log in to unmask] Date: December 02, 2021 Subject: DFO-TR, Derivative-free Optimization Solver 10 years ago, Katya and I wrote a Matlab code for continuous optimization of functions without using their derivatives. The code was called DFO-TR and has been used by colleagues and collaborators. DFO-TR runs a trust-region interpolation based method. It is essentially described in Section 5 of the paper A. S. Bandeira, K. Scheinberg, and L. N. Vicente, Computation of sparse low degree interpolating polynomials and their application to derivative-free optimization, Mathematical Programming, 134 (2012) 223-257. We are now releasing DFO-TR to the community (please see https://coral.ise.lehigh.edu/lnv/dfo-tr). Version 0.1 (November 2021) is written in Matlab and solves small, unconstrained DFO problems efficiently and robustly. DFO-TR is freely available for research, educational or commercial use, under a GNU lesser general public license. DFO-TR team: Liyuan 'Leon' Cao (Peking University) Tommaso Giovannelli (Lehigh University) Katya Scheinberg (Cornell University) Oumaima Sohab (Lehigh University) Luis Nunes Vicente (Lehigh University) ------------------------------------------------------- From: Kris ONeill [log in to unmask] Date: December 03, 2021 Subject: New Book, Modern Nonconvex Nondifferentiable Optimization Modern Nonconvex Nondifferentiable Optimization by Ying Cui and Jong-Shi Pang Starting with the fundamentals of classical smooth optimization and building on established convex programming techniques, this research monograph provides a foundation and methodology for modern nonconvex nondifferentiable optimization by providing readers with theory, methods, and applications of nonconvex and nondifferentiable optimization in statistical estimation, operations research, machine learning, and decision making. A comprehensive and rigorous treatment of this emergent mathematical topic is urgently needed in today's complex world of big data and machine learning. This book takes a thorough approach to the subject and includes examples and exercises to enrich the main themes, making it suitable for classroom instruction. SIAM Bookstore: https://my.siam.org/Store/Product/viewproduct/?ProductId=39767459 November 2021 / xx + 756 pages / Hardcover / 978-1-611976-73-1 / List $119.00 / SIAM Member $83.30 / MO29 ------------------------------------------------------- From: GEORGE ANASTASSIOU [log in to unmask] Date: December 03, 2021 Subject: New Book, Unification of Fractional Calculi with Applications UNIFICATION OF FRACTIONAL CALCULI WITH APPLICATIONS GEORGE A. ANASTASSIOU In this monograph we demonstrate the unifying methods of generalized versions of Hilfer, Prabhakar and Hilfer-Prabhakar fractional calculi and we establish related unifying fractional integral inequalities of the following types: Iyengar, Landau, Polya, Ostrowski, Hilbert-Pachpatte, Hardy, Opial, Csiszar's f-Divergence, self-adjoint operator and related to fuzziness. Our results are univariate and multivariate. This monograph's results are expected to find applications in many areas of pure and applied mathematics, especially in fractional inequalities and fractional differential equations. Other interesting applications can be in applied sciences like geophysics, physics, chemistry, economics and engineering. This monograph is appropriate for researchers, graduate students, practitioners and seminars of the above disciplines, also to be in all science and engineering libraries. https://www.springerprofessional.de/en/unification-of-fractional-calculi-with-applications/19887148 ------------------------------------------------------- From: Michael Dumbser [log in to unmask] Date: December 03, 2021 Subject: Numerical Methods for Hyperbolic PDE Course, ONLINE, Jan 2022 Short Course on Advanced Numerical Methods for Hyperbolic Equations Laboratory of Applied Mathematics, University of Trento, Italy The course will also be made available online via ZOOM Lecturers: Prof. Dr.-Ing. Michael Dumbser and Dr. Firas Dhaouadi Dates: January 31st - February 4th 2022 Website: http://www.unitn.it/nm2022 Course fee: 250 EUR (online), other fees for onsite participation This advanced short course is primarily designed for PhD students and post-doctoral researchers in applied mathematics, scientific computing, computational physics and computational mechanics. Summary: The course consists in a structured intensive one-week programme of 40 hours of theoretical lectures and computer laboratory exercises on advanced numerical methods for hyperbolic partial differential equations with applications in engineering and science. The course covers high order finite volume and discontinuous Galerkin methods, also on unstructured triangular simplex meshes; Riemann solvers, higher order TVD, ENO and WENO schemes, the discretization of hyperbolic PDE with non-conservative products, well balanced schemes as well as meshless Lagrangian particle methods. The lectures are supplemented with many computer laboratory exercises to provide practical hands-on experience concerning the practical aspects of the implementation of these advanced numerical methods. Further information can be found on the dedicated web page http://www.unitn.it/nm2022 The deadline for registration is January 30th 2022. For further questions, please contact: Prof. Dr.-Ing. Michael Dumbser, [log in to unmask] ------------------------------------------------------- From: Pedro Valero-Lara [log in to unmask] Date: December 03, 2021 Subject: Extreme Heterogeneity Solutions, South Korea, April 2022 ExHET 2022: The 1st International Workshop on Extreme Heterogeneity Solutions to be held in conjunction with PPoPP 2022, April 2-5, 2022, Seoul, South Korea CFP links: https://excl.ornl.gov/ppopp-exhet-2022/ https://easychair.org/cfp/ExHET22 Submission link: https://easychair.org/my/conference?conf=exhet22# While computing technologies have remained relatively stable for nearly two decades, new architectural features, such as specialized hardware, heterogeneous cores, deep memory hierarchies, and near-memory processing, have emerged as possible solutions to address the concerns of energy-efficiency, manufacturability, and cost. However, we expect this 'golden age' of architectural change to lead to extreme heterogeneity and it will have a major impact on software systems and applications. In this upcoming exascale and extreme heterogeneity era, it will be critical to explore new software approaches that will enable us to effectively exploit this diverse hardware to advance science, the next- generation systems with heterogeneous elements will need to accommodate complex workflows. This is mainly due to the many forms of heterogeneous accelerators (no longer just GPU accelerators) in this heterogeneous era, and the need of mapping different parts of an application onto elements most appropriate for that application component. ------------------------------------------------------- From: Pamela Bye [log in to unmask] Date: December 03, 2021 Subject: Mathematics of Finance, UK, Jun 2022 8-10 June 2022, Holiday Inn, Liverpool, UK https://ima.org.uk/17964/ima-conference-on-mathematics-of-finance-and-climate-risk/ This conference on financial mathematics will be focusing on pressing challenges in finance and insurance produced by climate change, demographic developments, and the ever-increasing dominance of data and information. Alongside the challenges, this three-day conference's goal will devote time to the role and responsibilities financial and insurance mathematics have in developing solutions and in assisting with transitions necessary to mitigate irreversible adverse environmental and socio- economic impact. The aim is to promote interdisciplinary cooperation bridging mathematics, statistics and computer science with finance, climate science, insurance and economics. Industry professionals and members of government agencies will be invited to share their experience and expertise alongside academic experts in the field in order to scope out the most urgent research directions with the highest potential for incisive solutions. Talks will be accepted for the conference based on a 350 word abstract. Abstracts should be submitted by 15th January 2022 either online at http://online.ima.org.uk or by e-mail to [log in to unmask] Authors will be notified by 15th February 2022 if their abstract has been accepted for oral or poster presentation. Oral presentations are expected to be 25 minutes in length, including time for questions and answers. Abstracts should include: Whether your title is intended for oral or poster presentation; Title of the talk; Authors and Affiliations; Intended Speaker; 350 words describing talk. Registration is currently open: https://my.ima.org.uk/services.php?section=events For scientific queries please contact: Paul Johnson, [log in to unmask] . For general conference queries please contact Maya Everson, Conference Officer E-mail: [log in to unmask] . ------------------------------------------------------- From: Ronnie Sircar [log in to unmask] Date: December 01, 2021 Subject: Assistant Professor Position, ORFE, Princeton Univ The Department of Operations Research and Financial Engineering (ORFE) at Princeton University invites applications for a tenure-track faculty appointment at the Assistant Professor level starting September 1, 2022. The search is in the area of Optimization & Operations Research, connecting with ongoing and planned strategic initiatives in the School of Engineering and Applied Science. In particular: HealthTech (for instance, optimal vaccine rollout strategies); Energy and the Environment (for instance, integrating renewable energy production, and optimizing the electricity grid of the future); Robotics and Cyberphysical Systems (uncertainty quantification, safety verification, and joint learning and control of dynamical systems); Resilient and Smart Cities (how smart cities could use information technologies for efficient deployment and utilization of perishable resources); and Data Science (for instance, optimization of machine learning algorithms). A PhD in a related field is required. To be successful, the candidate must have a strong commitment to excellence in research and in teaching at both the undergraduate and graduate levels. The ORFE department believes that the diversity of our faculty, staff, and students is essential to the distinction and excellence of our research and academic programs. To that end, we are eager to have a colleague who supports our institutional commitment to ensuring Princeton is inclusive, equitable, and diverse. The ORFE department is part of the School of Engineering and Applied Science and involved in activities with the Center for Statistics and Machine Learning, the Bendheim Center for Finance, the Program in Applied and Computational Mathematics, and the Andlinger Center for Energy and the Environment. An appointment may be made jointly with another department or program. Applications will be considered on a continuing basis, but candidates are encouraged to apply by December 15, 2021. To apply, please submit an online application at https://www.princeton.edu/acad-positions/position/23641. All applicants should include a CV, research statement, teaching statement, and contact information for at least three references, one of whom should be able to address the candidate's teaching abilities. ------------------------------------------------------- From: Sarah Dance [log in to unmask] Date: December 03, 2021 Subject: Postdoc Position, Data Assimilation, Univ of Reading The Department of Meteorology at the University of Reading seeks applications for a post-doctoral research role which will provide an exciting opportunity for the post-holder to contribute to and develop research work on observation impact in hazardous weather prediction: measuring the ability of different observation types to improve forecasts of hazardous weather through data assimilation. The post is jointly funded by the University of Reading and the UK Met Office. In a changing climate, an improved ability to forecast hazardous weather is key to the management of risk for society. Observations play an essential role in numerical weather prediction but are expensive to obtain. Quantitative measures of the impact of observations on weather forecasts allow evaluation of the best use of currently available observations and the design of future observing networks, to help ensure that observational data is used in the most cost-effective way for the benefit of society and the economy. Recently, international collaborative research efforts have developed tools for quantifying the observation impact on global weather forecasts. However, these tools are not applicable to the next generation of regional hazardous numerical weather prediction systems due to nonlinearity and statistical sampling issues. The Met Office Next Generation (NG) system is expected to include high resolution ensemble forecasts, driven by high frequency, spatially dense observation datasets. New approaches are needed to deal with these large data volumes that take advantage of modern data science. The post-holder will co-create their research with scientists at the University of Reading and the UK Met Office. For further information and to apply see https://jobs.reading.ac.uk/displayjob.aspx?jobid=8884 Deadline for applications 4 January 2022. ------------------------------------------------------- From: HRUSHIKESH N MHASKAR [log in to unmask] Date: December 01, 2021 Subject: Postdoc Position, ML/Signal Processing, Claremont Graduate Univ A postdoc position is available to work on an NSF grant project with Hrushikesh Mhaskar (CGU, https://www.cgu.edu/people/hrushikesh- mhaskar/). The areas of research are (1) approximation theory/computational harmonic analysis (2) machine learning, and (3) signal processing. The post-doc will collaborate closely with Mhaskar in (1) developing applications for his theory and writing the necessary codes to carry out the applications successfully enough to result in publications in respectable journals/proceedings (2) doing literature search and grant proposal development, (3) mathematical discussions and presentations. The ideal candidate should have good programming skills, and a background in basic real and complex analysis, approximation theory, harmonic analysis, and numerical analysis. The deadline for applications on mathjobs (https://www.mathjobs.org/jobs?joblist-2718-17968) with cover letter, vita, statement of research interests, and three letters of recommendation is December 15, 2021, but applications will be considered after that until the position is filled. The position is from July 1, 2022, to June 30, 2023, but the dates are somewhat flexible. Claremont Graduate University is a member of the Claremont Colleges, a consortium of two graduate and five undergraduate institutions that collectively house over 50 mathematics faculty members. The individual institutions cooperate, through the Claremont Center of Mathematical Sciences, to form one of the largest mathematical science research communities in California. The Institute of Mathematical Sciences runs a PhD program in Mathematics as well as joint PhD programs with other universities in Southern California. In addition, there are often clinics sponsored by local industries. Postdocs are eligible for on campus housing: https://www.claremontcollegiateapartments.com/ ------------------------------------------------------- From: Marta D'Elia [log in to unmask] Date: December 01, 2021 Subject: Postdoc Position, Sandia Labs/CA, USA A postdoctoral position is available at Sandia National Laboratories in Livermore, California, starting immediately. We are looking for creative problem solvers with knowledge of machine learning, deep learning, and, more generally, with experience in scientific computing, evidenced by publications and codes. Knowledge and experience in Bayesian statistics and uncertainty quantification are also desirable. The work mainly involves the design of new machine learning algorithms for the prediction of material structure and properties, for given physical process conditions. It also includes the design and implementation of efficient optimal experimental design methods, in both deterministic and Bayesian settings. The position will involve extensive interaction with a large and diverse project team, code development and analysis, and demonstration of the developed technology in multiple applications. This project is focused on discovering new resilient materials and manufacturing processes via an artificial-intelligence-guided approach. Qualifications We Require: PhD in applied mathematics, computational science or engineering; Knowledge and expertise in machine learning and neural networks; Knowledge and expertise in computational science and code development. Qualifications We Desire: Knowledge of optimization, Bayesian statistics, and uncertainty quantification; Expertise in Python and C++; Experience in performing collaborative research; Excellent communication skills. To apply for this job, please, go to the Sandia's career page: https://www.sandia.gov/careers/career-possibilities/students-and-postdocs/internships-co-ops/postdoctoral-positions/ and click on "Livermore", then, select the position with Job Opening ID: 679662, and Posting Title: Postdoctoral Appointee - Machine Learning in Material Science. For more information, please, contact Marta D'Elia at [log in to unmask] ------------------------------------------------------- From: David Hyde [log in to unmask] Date: December 03, 2021 Subject: Postdoc Positions, Physical Simulation + Learning, Vanderbilt Univ One or more postdoctoral positions are available in David Hyde's group in the Department of Computer Science at Vanderbilt University in Nashville, TN, USA. The postdoctoral scholar will work on projects at the intersection of computational physics, computer graphics, learning, vision, applied mathematics, high-performance computing, etc. (there is flexibility depending on the candidate's background and interests). Potential research topics include simulating new or existing physical (e.g. solid/fluid) phenomena at exascale; developing new formulations for solid-fluid coupling; building vision/learning-based models for incorporating real-world data into physical simulations and thereby improving simulation accuracy; using learning techniques to power higher-fidelity real-time simulations/effects; etc. Please see the group website for examples of past projects, and please see the job posting for a full description and application. Candidates are reviewed on a first-come/rolling basis. Group website: https://dabh.io Job posting: https://www.vanderbilt.edu/postdoc/position-detail/?id=602 ------------------------------------------------------- From: Daniel Appelö [log in to unmask] Date: December 01, 2021 Subject: PhD Positions, Computational Math/Sci/Eng, Michigan State Univ Michigan State University's Department of Computational Mathematics, Science and Engineering (CMSE; https://cmse.msu.edu/) is currently accepting applications for its interdisciplinary PhD program in computational and data science. This program provides its graduates broad and deep knowledge of the fundamental techniques used in computational modeling and data science, significant exposure to at least one application domain, and the opportunity to conduct significant original research in algorithms and/or applications relating to computational and data science. A brief introduction to the program can be found at here, and a list of CMSE faculty and their research interests can be found at http://cmse.msu.edu/faculty. Additional information about the PhD program, as well as application information, can be found at http://cmse.msu.edu/apply. Please note that the deadline for application for the Fall 2022 cohort is January 2, 2022. Please forward this message along to any students that you think might be interested in our graduate program. ------------------------------------------------------- End of Digest **************************