Inverse Problems
Research Advisors for Inverse Problems
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Daniel Appelö , bioProfessor Daniel Appelö is a numerical analyst with an interest in computational techniques for solving differential equations fast and accurately. He is excited about applications in acoustics, electromagnetics, fluids, and more recently in quantum computing.
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John Burns , bioProfessor Burns' current research is focused on computational methods for modeling, control, estimation and optimization of complex systems where spatially distributed information is essential. This includes systems modeled by partial and delay differential equations. Recent applications include modeling and control of thermal fluids, design and thermal management systems and optimization of mobile sensor networks.
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Eric de Sturler , bioProfessor de Sturler's research focuses on numerical analysis for large-scale computational problems with an emphasis on fast solvers for linear and nonlinear systems, inverse problems and parameter estimation, optimization, and design, including iterative solvers and numerical linear algebra, randomization, stochastic methods, model reduction, and high performance computing with applications in computational mechanics, such structural optimization and computational fluid dynamics, tomography and image reconstruction, big data, computational physics, biology, and computer graphics.
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Ionut-Gabriel Farcas , bioProfessor Farcaș's research bridges scientific computing, high-performance computing, and computational physics. His work focuses on scientific machine learning, reduced and surrogate modeling, uncertainty quantification, and sparse grid and multi-fidelity methods. These computational techniques are designed to tackle complex, large-scale numerical simulations, such as those arising in turbulent transport in fusion devices or combustion processes in rocket engines.
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Serkan Gugercin , bioProfessor Gugercin studies computational mathematics, numerical analysis, and systems and control theory with a focus on data-driven modeling and model reduction of large-scale dynamical systems with applications to inverse problems, structural dynamics, material design, and flow control.
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Tao Lin , bioProfessor Tao Lin's main research interest is the numerical analysis on computational methods related with differential equations. He designs new numerical methods and carry out their convergence analysis. His recent research focuses on immersed finite element (IFE) methods that can solve interface problems of partial differential equation with interface independent meshes. He is also working on applying IFE methods to interface inverse problems via the shape optimization methodology.
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Mirjeta Pasha , bioDr. Pasha is an Assistant Professor with research interests on high dimensional (tensor) data analysis, regularization for inverse problems, uncertainty quantification, and high-performance computing. She develops computationally efficient methods and algorithms to solve large-scale problems that arise from an extensive list of applications in data science, medicine, and engineering.
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Johann Rudi , bioProfessor Johann Rudi's research is interdisciplinary and spans large-scale parallel iterative methods for nonlinear and linear systems, development and implementation of algorithms for high-performance computing (HPC) platforms, computational aspects of inverse problems, and quantification of uncertainties in the inferred parameters.
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Tim Warburton , bioProfessor Warburton holds the John K. Costain Chair in the College of Science at Virginia Tech and is a faculty member of both the Department of Mathematics and the Computational Modeling and Data Analytics program. His research interests include developing new parallel algorithms and methods that are used to solve PDE based physical modes on the largest supercomputers.
Researchers of Inverse Problems
Recently Retired Faculty
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Layne T. Watson , bioDr. Watson's research interests include numerical analysis; nonlinear programming; mathematical software; solid mechanics; fluid mechanics; image processing; parallel computation; bioinformatics.