SIAM News Blog

April Prize Spotlight: Omar Ghattas and Olav Møyner

Congratulations to these two members of the SIAM community, who were recently awarded the SIAG/GS Career Prize and Early Career Prize, respectively.

Omar Ghattas - SIAG/GS Career Prize

Omar Ghattas of the University of Texas at Austin was awarded the SIAM Activity Group on Geosciences Career Prize at the 2019 SIAM Conference on Mathematical & Computational Issues in the Geosciences (GS19), held March 11-14, 2019 in Houston, Texas. He received the award and delivered his talk, “Large-scale Bayesian Inversion for Geosciences Problems,” on March 12, 2019.

The SIAM Activity Group on Geosciences (SIAG/GS) awards the SIAG/GS Career Prize every two years to an outstanding senior researcher for broad and distinguished contributions to the solution of mathematical and computational problems in the geosciences. The award recognizes Ghattas for his groundbreaking contributions in analysis, methods, algorithms, and software for grand challenge computational problems in geosciences, and for his exceptional influence as mentor, educator, and collaborator.

Omar Ghattas is a Professor of Geological Sciences and Mechanical Engineering at the University of Texas at Austin. He is also the Director of the Center for Computational Geosciences and Optimization at the Institute for Computational Engineering and Sciences (ICES) and holds the John A. and Katherine G. Jackson Chair in Computational Geosciences. He is a member of the faculty in the Computational Science, Engineering, and Mathematics (CSEM) interdisciplinary PhD program in ICES, and holds courtesy appointments in Computer Science and Biomedical Engineering. He received his PhD from Duke University and held positions on the faculty of Carnegie Mellon University before he joined the faculty of the University of Texas at Austin in 2005.

Omar Ghattas received the SIAG/GS Career Prize at GS19
Ghattas has general research interests in forward and inverse modeling, optimization, and uncertainty quantification of large-scale complex mechanical, geological, and biological systems. With collaborators, he received the ACM Gordon Bell Prize in 2003 (for Special Achievement) and again in 2015 (for Scalability), and was a finalist for the 2008, 2010, and 2012 Bell Prizes. He and his co-authors received the 2019 SIAM Activity Group on Computational Science and Engineering Best Paper Prize. He is a Fellow of SIAM.

Q: Why are you excited to be awarded the SIAG/GS Career Prize?

A: SIAM Activity Group on Geosciences is a thriving community with a rich history of fundamental advances in mathematical and computational methods for geoscience problems, and I'm thrilled and honored to have been selected to receive this community's Career Prize. I give all the credit to the outstanding students, postdocs, and colleagues I have been fortunate enough to work with over the years!

Q: Could you tell us a bit about the research that won you the prize?

A: The geosciences are a tremendous source of computational grand challenges. The mathematical models that arise are often characterized by highly nonlinear behavior, strong heterogeneities and anisotropies, a wide range of length and time scales, complex geometries, and numerous uncertain parameters. Leading edge supercomputers must be employed to tackle these problems, and specialized algorithms that can efficiently scale on these complex architectures must be devised. I chose to focus my prize lecture on the specific challenges of uncertainty in models, and in particular the inference of model parameters within the framework of Bayesian inversion. The Bayesian framework offers a principled means of accounting for uncertainty in the solution of inverse problems, given uncertainty in observational data, forward models, and any prior information. However, Bayesian inversion has remained out of reach for many geoscience problems, due to the infinite-dimensional nature of typical parameter fields (e.g., initial and boundary conditions, heterogeneous material parameters, source terms) and the complexity of forward models of many geoscience processes. The lecture presented some recently-developed Bayesian inversion methods inspired by large-scale optimization ideas, which have shown promise in overcoming these challenges.

Q: What does your research mean to the public?

A: Inverse problems abound in all areas of science, engineering, medicine, and technology. The geosciences are perhaps the richest source of challenging inverse problems. As just a few examples: When you check the weather forecast, you've benefited from inverse problems that are solved with continuous streams of observations. When energy companies search for oil or manage reservoirs, they employ geophysical inverse methods. Earthquake sources are inferred from seismic and geodetic data, as is the structure of the Earth. Uncertain states and parameters of atmospheric, ocean, ice, and land components of climate models are inferred from historical and present day climate observations and proxy data. And subsurface contaminants are inferred by solving inverse problems. Beyond the geosciences, inverse problems directly impact the public in numerous areas, from medical imaging to calibration of models of engineered systems and processes (e.g., aerospace and automotive structures, chemical plants, electronics) for design and control purposes. Even the field of machine learning relies fundamentally on the solution of inverse problems, in the training of ML models on data.

Q: What does being a SIAM member mean to you?

A: Everything! First class conferences, journals, books, and – most important -- community.

Olav Møyner - SIAG/GS Early Career Prize

Olav Møyner of SINTEF, Norway, received the SIAM Activity Group on Geosciences Early Career Prize at the 2019 SIAM Conference on Mathematical and Computational Issues in the Geosciences (GS19), held March 11-14, 2019 in Houston, Texas. He was awarded the prize on March 12 and gave his lecture, “Multiscale Simulation of Porous Media Flow: Obstacles, Opportunities and Open-source,” on March 13, 2019.

The SIAM Activity Group on Geosciences (SIAG/GS) awards the SIAG/GS Early Career Prize to an individual in their early career for distinguished research contributions to the solution of mathematical and computational problems in the geosciences published in the three calendar years prior to the award year. The award recognizes Møyner for his elegant and insightful contributions to theory, algorithms, and software for multiscale porous flow simulation, and for his exceptional scholarly productivity and impact on practice.

Olav Møyner gave his lecture, “Multiscale Simulation of Porous Media Flow: Obstacles, Opportunities and Open-source,” at GS19
Olav Møyner earned his MSc in physics and mathematics and his PhD in mathematics, both from the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. He currently holds a postdoctoral position at NTNU funded by the VISTA program, a collaboration between Statoil and The Norwegian Academy of Sciences and Letters. He is also a research scientist in the Computational Geosciences group at SINTEF Digital. His work on multiscale methods for reservoir simulation won him the 2017 prize from the Dimitris N. Chorafas Foundation for the best PhD thesis at NTNU. Møyner has a broad interest in computational methods for flow in porous media, including gridding, discretizations and solvers, upscaling and multiscale methods, multiphase and compositional simulation, as well as flow diagnostics and other types of reduced-order modelling.

Q: Why are you excited to be winning the prize?

A: I’m honored to receive the SIAM/GS Early Career Prize as a recognition of the work my collaborators and I have done to develop new methods that make simulation of porous media flow more accurate and efficient, and to make advanced simulation methods more accessible to our peers through development and release of our open source community code.

Q: Could you tell us a bit about the research that won you the prize?

A: Primarily, I have developed improved multiscale solvers that can handle the complex grids, highly heterogeneous petrophysics, and strongly coupled multiphase and compositional flow physics seen in models of real assets. I have also worked on sequential formulations, and robust and efficient nonlinear solvers. To be able to develop these new methods, and test and demonstrate their potential in a real life setting, I have developed an open-source framework for reservoir simulation that seems to have become a community code. In addition, I have worked on so-called flow diagnostics, which are simple numerical methods you can use to quickly reveal volumetric communication within a reservoir, measure dynamic heterogeneity, etc.

Q: What does your research mean to the public?

A: Porous media flow is important for a number of engineering applications, including hydrocarbon recovery, geothermal energy, subsurface CO2 sequestration and gas storage, and modeling of groundwater flow. Improved accuracy and efficiency for simulating such processes can both greatly improve the confidence in prediction and be useful in obtaining an optimal outcome. The Computational Geosciences group at SINTEF has also made a commitment to releasing our research as freely available, open-source software. This has proven to be very popular both with others who want to understand and build upon our research, and with researchers in developing countries, where commercial packages are not economically feasible.

Q: What does participation in SIAM mean to you?

A: The SIAM Conference on Mathematical and Computational Issues in the Geosciences is a wonderful conference that highlights the breadth of mathematical geoscience research. It is always inspiring to see that disparate topics often have the same underlying mathematical challenges when working with complex systems. In addition, the numerous high-quality books and journals published by SIAM are indispensable to my work.

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