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Inverse Problems and Reduced Order Modeling for Wave Propagation Problems

Thomas Hagstrom, Southern Methodist University
Daniel Appelo, Michigan State University
Lu Zhang, Columbia University
Speakers in this minisymposium will discuss diverse applications of new algorithms to challenging problems in wave theory. Examples include the use of machine learning as well as traditional reduced order modeling techniques to develop effective preconditioners and fast surrogate direct models to solve inverse problems and to approximate multiscale media with uncertainty quantification. In addition new methods, such as time-domain solvers in the frequency domain and frequency-domain solvers in the time domain, will be considered.