Caro Professor(a)/Estudante,

 Conforme você já deve ser sido notificado anteriormente, neste semestre o PPGM (Programa de Pós-Graduação em Matemática da UFPR) está ofertando a disciplina Modelagem Matemática do Ciclo do Carbono, a qual está sendo ministrada pelo Prof. Francisco Virissimo, pesquisador do Centro Oceanográfico Nacional do Reino Unido. Entre as atividades da disciplina está  uma série de aulas especiais dadas por pesquisadores convidados de prestígio internacional

 Na próxima terça-feira, 22/06, das 14:00-16:00, teremos a última aula convidada com a Dra Irina Tezaur (Sandia National Laboratories, USA) , pelo Zoom. DraTezaur é atualmente Engenheira Senior do Quantitative Modeling & Analysis Department do Sandia National Laboratories  em Livermore (California), possui doutorado em Computational & Mathematical Engineeringpela Stanford University, e atua nas áreas de métodos numéricos para EDPs, elementosfinitos, modelagem do clima, entre outras. Informações sobre o tema da aula/palestra e mais detalhes sobre a carreira da Dra Tezaur  são dados mais abaixo. 

 A aula é aberta, qualquer pessoa interessada em conhecer aplicações da matemática na natureza é muito bem-vinda, basta preencher este formulário que enviaremos o link de acesso horas antes por e-mail.  

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Title: Mathematical Modeling of the Polar Ice Sheets 

Lecturer:  Dra.  Irina K. Tezaur (Sandia National Laboratories,USA) 

Abstract:
 Recent observations show that both the Greenland and Antarctic ice sheets are losing mass at increasingly rapid rates [1]. In its fourth assessment report (AR4), the Intergovernmental Panel on Climate Change (IPCC) declined to include estimates of future sea-level change from dynamics of the polar ice sheets due to the inability of ice sheet models to mimic or explain observed dynamic behaviors, such as the acceleration and thinning then occurring on several of Greenland’s large outlet glaciers [2].In recent years, there has been a push to develop “next generation” land-ice models and codes for integration into global Earth System Models (ESMs) to address this acknowledged limitation. This talk will give an overview of one suchnext-generation land-ice dynamical code (dycore) known as Albany Land-Ice (ALI)[3], currently under development at Sandia National Laboratories, focusing onthe mathematical and computational algorithms underlying this model. Unlikemany of its predecessors, ALI: (1) is able to perform realistic,high-resolution, continental scale simulations, (2) is robust, efficient and scalable on next-generationhybrid systems (multi-core, many-core, GPU, Intel Xeon Phi), (3) possessesbuilt-in advanced analysis capabilities (e.g., sensitivity analysis, optimization,uncertainty quantification), and (4) is hooked up to the U.S. Department ofEnergy’s Energy Exascale Earth System Model (E3SM) as well as NCAR’s CommunityEarth System Model (CESM).

The ALIdycore is based on the so-called “First-Order Stokes” equations for the ice momentum balance [4], an attractive alternate to the more expensive “FullStokes” model. Both the Full Stokes and the First-Order Stokes models assumethat ice behaves like a very viscous, shear-thinning, non-Newtonian fluid,similar to lava flow. Following an overview of our land-ice model and project,I will describe some of the mathematical algorithms and computational softwarewe have developed as a part of this project that have contributed to ourdycore’s robustness and scalability. These include robust automatic-differentiation-based nonlinear solvers, scalablealgebraic-multigrid-based iterative linear solvers [5], and stable semi-implicit First-Order Stokes-thickness coupling methods. I will also discuss some of the advanced analysis capabilities in ALI, namely a large-scaleinversion approach we have developed for obtaining optimal ice initialconditions [6], our workflow towards quantifying uncertainties in land-icemodels, and performance-portability of the ALI code to new and emerging architectures usingthe Kokkos library [7,9]. I will show results which demonstrate that the ALIdycore is scalable, fast and robust for production-scale land-ice problems onstate-of-the-art HPC machines. I will also discuss results from a recent validation study in which ALI was used to simulate the Greenland ice sheet during the period 1991-2013 with realistic climate forcing, and the simulation datawere compared with observational data collected by NASA satellites [8].Finally, I will show some predictive dynamic experiments and simulations we are beginning to perform using ALI.

This work was done in collaboration with Luca Bertagna, Max Carlson, Irina Demeshko, MikeEldred, Matt Hoffman, John Jakeman, Mauro Perego, Steve Price, Andy Salinger,Chad Sockwell, Ray Tuminaro and Jerry Watkins.

 References:
1. I. Velicogna. Increasingrates of ice mass loss from the Greenland and Antarctic ice sheets revealed byGRACE. Geophysical Research Letters, 36 (19) L19503, 2009.
2. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. Averyt, M. Tignor,H. Miller. Climate change 2007: The physical science basis, Contribution ofWorking Group I to the Fourth Assessment Report of the Intergovernmental Panelon Climate Change, Cambridge Univ. Press, Cambridge, UK, 2007.
3. I. Tezaur, M. Perego, A. Salinger, R. Tuminaro, S. Price. Albany/FELIX: AParallel, Scalable and Robust Finite Element Higher-Order Stokes Ice SheetSolver Built for Advanced Analysis, Geosci. Model Develop. 8 1-24, 2015.
4. J.K. Dukowicz, S.F. Price, W. Lipscomb. Consistent approximations andboundary conditionsfor ice-sheet dynamics from a principle of least action. J.Glaciol., 56(197) 480-496, 2010.
5. R. Tuminaro, M. Perego, I. Tezaur, A. Salinger, S. Price. A matrix dependent/algebraic multigridapproach for extruded meshes with applications to ice sheet modeling, SIAMJ. Sci. Comput. 38 (5) C504-C532, 2016.
6. M. Perego, S. Price, G. Stadler. Optimal initial conditions for coupling icesheet models to earth system models,J. Geophys. Res., 119 1894-1917, 2014. 7. H.C. Edwards, C.R. Trott, D.Sunderland. Kokkos: Enabling manycore performance portability throughpolymorphic memory access patterns. J. Par. and Distr. Comput., 74(12)3202?3216, 2014.
8. S. Price, M. Hoffman, J. Bonin, T. Neumann, I. Howat, J. Guerber, I. Tezaur,J. Saba, J. Lanaerts, D. Chambers, W. Lipscomb, M. Perego, A. Salinger, R.Tuminaro. An ice sheet model validation framework for the Greenland ice sheet, Geosci.Model Dev. 10 255-27, 2017.
9. J. Watkins, I. Tezaur, I. Demeshko. “A study on the performance portabilityof the finite element assembly process within the Albany land ice solver",E. van Brummelen, A. Corsini, S. Perotto, G. Rozza, eds. Numerical Methodsfor Flows: FEF 2017 Selected Contributions, Elsevier, 2019.   


Lecturer profile: 
Dr. Tezauris currently a Principal Member of Technical Staff in the Quantitative Modeling& Analysis Department at Sandia National Laboratories in Livermore, California. She began her research career at Sandia in 2007. Since then, she has had the opportunity to experience research in three of the lab’s centers, the Engineering Sciences Center, the Center for Computing Research and theCenter for Homeland Security, spanning Sandia’s two main sites (Albuquerque, NMand Livermore, CA). Dr. Tezaur has published 25 peer-reviewed articles and more than 20 technical reports, white papers and conferencepapers, and has been a lead developer of several open-source codes. In 2019, Dr. Tezaur was awarded the Presidential Early Career Award for Scientists and Engineers for “developing new, impactful mathematical methods and computer algorithmsto enable real-time analysis, control and decision-making on computationallyprohibitive problems relevant to the nuclear security mission and
climate modeling". Dr. Tezaur holds a Ph.D. in Computational &Mathematical Engineering from Stanford University, in addition to a B.A. andM.A. in Mathematics from the University of Pennsylvania. Her research focuses broadly on developing algorithms and software to enable the modeling and simulationof complex multi-scale and multi-physics problems using high-performance computing. Her research interests include numerical solution to partial differential equations (PDEs), mixed/hybrid finite elements, reduced order modelling(ROM), multi-scale coupling methods, and climate modeling.