This is the second mini-symposium for the Emergent Data Science program, Bridging the Gap: From Computational Physics, to Physics-informed Machine Learning, to Data-driven Scientific Discovery. The program aims to bring together experts in numerical simulation and data science to explore the intersection between, and bridge the gap separating, physics-based models grounded in first principles and data-driven models based on machine learning techniques.
| April 21, 2026 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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2:10-2:15 |
Opening Remarks
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2:15-2:40 |
Are We There Yet? Modeling of Multimessenger Signals in the Plasma Universe Bart Ripperda, Canadian Institute for Theoretical Astrophysics, Faculty of Arts and Science |
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2:40-3:05 |
All Quiet on the Numerical Front? Predictable Convergence in Computational Finance Christina Christara, Department of Computer Science, Faculty of Arts and Science |
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3:05-3:30 |
Scientific machine learning for electromagnetic field computations
Costas Sarris, Edward S. Rogers Sr. Department of Electrical & Computer Engineering, Faculty of Applied Science & Engineering |
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3:30-3:55 |
Projection-based model reduction for rapid and reliable solution of many-query aerodynamics problems
Masayuki Yano, Institute for Aerospace Studies, Faculty of Applied Science &Engineering |
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3:55-4:00 |
Closing remarks |
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Christina Christara
Department of Computer Science, Faculty of Arts and Science, University of Toronto
Prof. Christara works on the development, analysis, implementation and evaluation of efficient and accurate numerical methods for the solution of partial differential equations. Her recent research focuses on applications in computational finance, addressing challenges such as discontinuities in initial conditions, nonlinearities in PDEs, free-boundary problems, and high dimensionality. Her work aims to design numerical methods with predictable convergence behavior that remain computationally tractable for large-scale problems. She has served as Editor-in-Chief of Mathematics and Computers in Simulation and currently serves as Associate Editor of that journal as well as of Applied Mathematics and Computation. She serves as Secretary of the Canadian Applied and Industrial Mathematics Society (CAIMS).
Bart Ripperda
Canadian Institute for Theoretical Astrophysics, Faculty of Arts & Science University of Toronto
Prof. Ripperda’s research focuses on the fundamental plasma physics of neutron star and black hole magnetospheres, jets, coronae and accretion disks to understand their high-energy emission. Bart is a plasma physicist by nature, and uses the universe as a plasma physics laboratory.
Bart is currently most excited about the multimessenger and multiwavelength signals that are being observed from isolated and binary black holes and neutron stars. Modeling and understanding these signals require a combination of theoretical and numerical (general) relativistic kinetic and fluid methods to capture the large astrophysical scales and the microscopic particle scales where the emission is powered. The universe, and in particular the environment of neutron stars and black holes is a testbed for energy, mass, and length scales that are unreachable on Earth. This synergy and combination of different areas of physics (quantum electrodynamics, general relativity, plasma physics, particle physics) that is required to understand the regions of extreme gravity and energy is what drives his curiosity.
Costas Sarris
Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto
Prof. Sarris’s research area is computational electromagnetics, with an emphasis on time-domain modeling. He works on physics-based wireless propagation models (with full-wave, asymptotic, and hybrid techniques), uncertainty quantification, and scientific machine learning. Dr. Sarris is an IEEE Fellow and a Distinguished Lecturer of the IEEE Antennas and Propagation Society for 2024-2026. In 2019-2024, he was the Editor-in-Chief of the IEEE Journal on Multiscale and Multiphysics Computational
Techniques.
Masayuki Yano
Institute for Aerospace Studies, Faculty of Applied Science and Engineering, University of Toronto
Prof. Yano’s research interests lie in the development of numerical methods and the associated mathematical theories for partial differential equations. His current work focuses on adaptive high order methods, projection based model reduction, other data driven surrogate modeling techniques, and uncertainty quantification, with applications to continuum mechanics problems.
Data Sciences Institute,
Seminar room
10th floor,
700 University Avenue
