2015
Prof. Levent Gürel received the Harrington-Mittra Award in Computational Electromagnetics.

  2014
New book from IEEE Press and Wiley: The Multilevel Fast Multipole Algorithm (MLFMA) for Solving Large-Scale Computational Electromagnetics Problems.

  2014
Another achievement in the series of world record: More than one billion unknown solved.

  2013
Prof. Levent Gürel received 2013 UIUC ECE Distinguished Alumni Award.

  2011
Prof. Gürel is elevated to ACES Fellow Grade.

  2011
Prof. Gürel is invited to address the 2011 ACES Conference in Virginia, USA, as a Plenary Speaker.

  2011
Prof. Levent Gürel is named IEEE Distinguished Lecturer.

   2009
Prof. Levent Gürel is elevated to IEEE Fellow grade


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Prof. Levent Gürel is Named IEEE Distinguished Lecturer (DL)



Prof. Levent Gürel (IEEE Fellow) is elected as a Distinguished Lecturer of the IEEE Antennas and Propagation Society for 2011-2014.


The Institute of Electrical and Electronics Engineers (IEEE) has elected Prof. Levent Gürel as a Distinguished Lecturer of the Antennas and Propagation Society for 2011-2014. With more than 400,000 members, IEEE is the world's largest technical professional organization. Each year, technical societies of IEEE choose groups of select individuals as Distinguished Lecturers, who are engineering professionals leading their fields in new technical developments. During the next three years, Prof. Gürel is expected to share his expertise with researchers all over the world by delivering lectures on state-of-the-art topics such as parallel computing in electromagnetics, iterative and parallel solvers and preconditioners for extremely large matrix equations, and solutions to the world’s largest integral-equation problems.

Prof. Levent Gürel, of the Department of Electrical and Electronics Engineering (EEE), is also a Fellow of IEEE. Elevation to the Fellow rank is one of the highest honors that can be bestowed upon an individual by the IEEE; less than 0.1 percent of all IEEE members worldwide are awarded this title every year. Prof. Gürel was named IEEE Fellow to recognize his extraordinary contributions to fast methods and algorithms for computational electromagnetics.

Prof. Gürel serves as the director of the Bilkent University Computational Electromagnetics Research Center (BiLCEM). Since 2006, BiLCEM researchers have been setting world records by solving extremely large integral-equation problems involving hundreds of millions of unknowns. The most recent record set by BiLCEM required solving 550,000,000 x 550,000,000 dense matrix equations. This superlative work is an outcome of a multidisciplinary study involving physical understanding of electromagnetics problems, developing novel parallelization strategies (computer science), constructing parallel clusters (computer architecture), and employing advanced mathematical methods for integral equations, fast solvers, iterative methods, preconditioners, and linear algebra. Computational science and parallel computing are not the only disciplines to benefit from such an incredible achievement; the ultimate goal is to apply this huge computational capability to find solutions to previously intractable physical, real-life, and scientific problems in important areas such as medical imaging, bioelectromagnetics, metamaterials, nanotechnology, radars, antennas, wireless communications, remote sensing, optics, and many other disciplines involving electromagnetic fields, acoustic waves, and even quantum physics.

Levent Gürel received the B.Sc. degree from METU in 1986 and the M.S. and Ph.D. degrees from the University of Illinois at Urbana-Champaign (UIUC) in 1988 and 1991, respectively, all in electrical engineering. He joined the Thomas J. Watson Center of the IBM Research Division, Yorktown Heights, New York, in 1991. Since 1994, he has been a faculty member in the Department of Electrical and Electronics Engineering of Bilkent University. Currently, he is also an adjunct professor at UIUC.

Prof. Gürel's accomplishments are recognized in Turkey with two prestigious awards from the Turkish Academy of Sciences (TUBA) in 2002 and the Scientific and Technical Research Council of Turkey (TUBITAK) in 2003. He is currently serving as an associate editor for Radio Science, IEEE Antennas and Wireless Propagation Letters (AWPL), Journal of Electromagnetic Waves and Applications (JEMWA), and Progress in Electromagnetics Research (PIER).

Prof. Gürel served as the Chairman of the AP/MTT/ED/EMC Chapter of the IEEE Turkey Section from 2000 to 2003. He founded the IEEE EMC Chapter in Turkey in 2000. He served as the co-chair of the 2003 IEEE International Symposium on Electromagnetic Compatibility. He was the organizer and general chair of the CEM’07 and CEM’09 Computational Electromagnetics International Workshops held in 2007 and 2009.

Even before he was named an IEEE Distinguished Lecturer, Prof. Gürel presented more than 30 invited talks all over the world. He has been invited to the U.S. state of Virginia to deliver the Plenary Address at the 2011 Applied Computational Electromagnetics Society (ACES) Conference in March.


Abstracts of DL Presentations


Solution of Extremely Large Integral-Equation Problems in Computational Electromagnetics

Accurate simulations of real-life electromagnetics problems with integral equations require the solution of dense matrix equations involving millions of unknowns. Solutions of these extremely large problems cannot be achieved easily, even when using the most powerful computers with state-of-the-art technology. However, with MLFMA and parallel MLFMA, we have been able to obtain full-wave solutions of scattering problems discretized with hundreds of millions of unknowns. Some of the complicated real-life problems (such as, scattering from a realistic aircraft) involve geometries that are larger than 1000 wavelengths. Accurate solutions of such problems can be used as reference data for high-frequency techniques. Solutions of extremely large canonical benchmark problems involving sphere and NASA Almond geometries will be presented, in addition to the solution of complicated objects, such as metamaterial problems, red blood cells, and dielectric photonic crystals. For example, by solving the world’s largest and most complicated metamaterial problems (without resorting to homogenization), we demonstrate how the transmission properties of metamaterial walls can be enhanced with randomly-oriented unit cells. Also, we present a comparative study of scattering from healthy red blood cells (RBCs) and diseased RBCs with deformed shapes, leading to a method of diagnosis of blood diseases based on scattering statistics of RBCs. We will present solutions of extremely large problems involving more than one billion (!) unknowns.


Efficient Parallelization of the Multilevel Fast Multipole Algorithm (MLFMA)
It is possible to solve electromagnetics problems several orders of magnitude faster by using MLFMA. Without exaggeration, this means accelerating the solutions by thousands or even millions of times, compared to the Gaussian elimination. However, it is quite difficult to parallelize MLFMA. This is because of the already-too-complicated structure of the MLFMA solver. Recently, we have developed a hierarchical parallelization scheme for MLFMA. This novel parallelization scheme is both efficient and effective. This way, we have been able to parallelize MLFMA over hundreds of processors. By using distributed-memory architectures, this accomplishment translates into an ability to use more memory and to solve much larger problems than it was possible before. Unlike previous parallelization techniques, with the novel hierarchical partitioning strategy, the tree structure of MLFMA is distributed among processors by partitioning both clusters and samples of fields at each level. Due to the improved load-balancing, the hierarchical strategy offers a higher parallelization efficiency than previous approaches, especially when the number of processors is large. We demonstrate the improved efficiency on scattering problems discretized with millions of unknowns. We present the effectiveness of our algorithm by solving very large scattering problems.


Novel and Effective Preconditioners for Iterative Solvers
Solutions of extremely large matrix equations require iterative solvers. MLFMA accelerates the matrix-vector multiplications performed with every iteration. Despite the acceleration provided by MLFMA, the number of iterations should also be kept at a minimum, especially if the dimension of the matrix is in the order of millions. This is exactly where the preconditioners are needed. We have developed several novel preconditioners that can be used to accelerate the solution of various problems formulated with different types of integral equations. For example, it is well known that the electric-field integral equation (EFIE) is worse conditioned than the magnetic-field integral equation (MFIE) for conductor problems. Therefore, the preconditioners that we develop for EFIE are crucial for the solution of extremely large EFIE problems. For dielectric problems, we formulate several different types of integral equations to investigate which ones have better conditioning properties. Furthermore, we develop effective preconditioners specifically for dielectric problems. In this talk, we will review three classes of preconditioners:

1. Sparse near-field preconditioners
2. Approximate full-matrix preconditioners
3. Schur complement preconditioning for dielectric problems

We will present our efforts to devise effective preconditioners for MLFMA solutions of difficult electromagnetics problems involving both conductors and dielectrics, such as the block-diagonal preconditioner (BDP), incomplete LU (ILU) preconditioners, sparse approximate inverse (SAI) preconditioners, iterative near-field (INF) preconditioner, approximate MLFMA (AMLFMA) preconditioner, the approximate Schur preconditioner (ASP), and the iterative Schur preconditioner (ISP).


Parallel-MLFMA Solutions of Large-Scale Problems Involving Dielectric and Composite Metamaterial Structures
It is possible to solve extremely large electromagnetics problems accurately and efficiently by using the multilevel fast multipole algorithm (MLFMA). This has important implications in terms of obtaining the solution of previously intractable physical, real-life, and scientific problems in various areas, such as (subsurface) scattering, optics, bioelectromagnetics, metamaterials, nanotechnology, remote sensing, etc. Accurate simulations of such real-life electromagnetics problems with integral equations require the solution of dense matrix equations involving millions of unknowns. Most recently, we have achieved the solution of 1,000,000,000x1,000,000,000 dense matrix equations! For more information, please visit www.abakus.computing.technology.

In this seminar, following a general introduction to integral-equation and MLFMA formulations of dielectric/composite structures, I will continue to present rigorous modeling of three-dimensional optical metamaterial and plasmonic structures that are composed of multiple coexisting dielectric and/or conducting parts. Such composite structures may possess diverse values of conductivities and dielectric constants, including negative permittivity and permeability. Several different types of integral equations will be presented to investigate which ones have better conditioning properties. I will mention the development of effective Schur complement preconditioners specifically for dielectric problems. The hierarchical strategy is used for the efficient parallelization of MLFMA on distributed-memory architectures. Furthermore, various challenges encountered during the solution of complicated real-life problems will be addressed.


A Class of Algebraic and Kernel-Dependent Fast Solvers for Integral Equations in Computational Electromagnetics
Our research has been targeting the solution of the world’s largest integral-equation problems in computational electromagnetics. Most recently, we have achieved the solution of 1,000,000,000x1,000,000,000 (one billion!) dense matrix equations! Solutions of extremely large problems reported in the literature during the past decade are obtained with the fast multipole methods, not only very efficiently, but also very accurately. Accuracy is due to the rigor of the integral equations and the error-controllable nature of the multipole solvers. Fast multipole solvers are kernel-dependent techniques, i.e., they rely on certain analytical properties of the IE kernel, such as diagonalizability. Efficiency of the multiple solvers due to their ability to take advantage of the underlying rank-deficient blocks of the system matrix (a.k.a. the “impedance matrix”). Algebraic fast solvers are also based on the same idea, indeed, in a more obvious manner.

In this seminar, following a general introduction to our work in computational electromagnetics, I will continue to present fast and accurate solutions of large-scale electromagnetic modeling problems involving three-dimensional geometries with arbitrary shapes using the multilevel fast multipole algorithm (MLFMA), parallel MLFMA, adaptive cross approximation (ACA), and hierarchical (H) matrices. Solutions of extremely large matrix equations require iterative solvers. MLFMA accelerates the matrix-vector multiplications required in every iteration. H matrices provide fast solutions of low-frequency problems, for which MLFMA may fail. ACA can be used for the efficient solution of matrix equations with multiple right-hand-sides (RHSs). Solving the world's largest computational electromagnetics problems has important implications in terms of obtaining the solution of previously intractable physical, real-life, and scientific problems in various areas, such as (subsurface) scattering, optics, bioelectromagnetics, metamaterials, nanotechnology, remote sensing, etc. Accurate simulations of such real-life electromagnetics problems with integral equations require the solution of dense matrix equations involving millions of unknowns. For more information, please visit www.abakus.computing.technology.