Aerospace Computational Engineering Lab
Professor Masayuki Yano
I am an associate professor at the UTIAS. My research interests lie in numerical methods, scientific computation, and numerical analysis for partial differential equations (PDEs) in continuum mechanics. I currently work on adaptive high-order methods, reduced-order modeling, and model-data synthesis. I obtained BS in Aerospace Engineering from Georgia Tech, SM in Computation for Design and Optimization from MIT, and PhD in Aeronautics and Astronautics from MIT. I was a postdoc in the Department of Mechanical Engineering at MIT before joining UTIAS in Fall 2015.
I am PhD student in the Aerospace Computational Engineering Lab at UTIAS; my research focuses on numerical methods and surrogate modelling for stochastic partial differential equations. I completed my M.A.Sc. in the same lab in 2018, and prior to that I completed a B.A.Sc. in Engineering Physics at Queen's University in Kingston, Ontario.
I started my PhD studies in January 2021 under Prof. Yano's supervision as a Flex-time PhD student; simultaneously working at Autodesk Research as a research scientist. My PhD thesis is focused on multi-scale reduced order modeling of partial differential equations. My other areas of interest include numerical analysis, structural optimization and simulation, and physics-informed machine learning. I obtained a master's degree from the University of Toronto in 2015 and another master's degree from Sharif University of Technology (Iran) in 2011, both in mechanical engineering. I also received a bachelor's degree in mechanical engineering from Sharif University of Technology in 2009.
(Co-supervised by Professor Nair)
I am a PhD candidate co-supervised by Prof. Masayuki Yano and Prof. Prasanth Nair at UTIAS. My research topic is robust topology optimization for linear elastic structures. This work focuses on analysis of optimization algorithms, as well as analysis of loading. material and geometric uncertainty within the optimization framework. I obtained my BSE in Aerospace Engineering from the University of Michigan in 2014, and my MSc in Advanced Computational Methods for Aeronautics from Imperial College London, under the supervision of Prof. Francesco Montomoli, in 2015.
(Co-supervised by Adam Steinberg @ Georgia Tech)
My research interests lie at the interface between experimental fluid mechanics and numerical methods. In particular, I am interested in physics-aware data inversion methods for experiments involving turbulent flows. Currently I am working on an inverse uncertainty quantification framework for a supercritical carbon dioxide mixing layer experiment. I obtained a Bachelors of Engineering in Honours Mechanical Engineering from McGill University and a Masters of Applied Science from UTIAS.
I am an MASc student in the Aerospace Computational Engineering Lab at UTIAS and co-supervised by Adam Steinberg at Georgia Tech. I began my MASc in fall 2019 and my research focuses on fluid flow velocimetry methods with applications in turbulent flow research. My current project is the formulation of a physics-informed three-dimensional three-component partical image intensity-based velocimetry method. I obtained a Honours Bachelors of Science as a Physics Specialist at the University of Toronto.
I am an MASc student at UTIAS. My research is on the development of non-linear reduced basis approximation methods for partial differential equations with moving discontinuities. The focus of my work is on developing reduced order methods for robust approximation of transonic flows. I completed my BASc in aerospace engineering at the University of Toronto. During my BASc I was the director of the rocketry division of the University of Toronto Aerospace Team, and accrued several years of experience in developing rocket technology and managing large teams.
- Adrian Humphry, 2022, "Efficient hyperreduction by empirical quadrature procedure with constraint reduction for large-scale parameterized nonlinear problems".
- Ben Gibson, 2022, "Accelerated PDE-constrained optimization by adaptive reduced order modelling and goal-oriented hyperreduction".
- Anthony Webster, 2021, "Enriched reduced model accelerate level-set topology optimization".
- George Lu, 2021, "A high-resolution optical flow inspired 3D3C velocimetry method based on sparse representation of 3D particle images".
- Andrew Ilersich, 2021, "Reducing the cost of ensemble-based data assimilation in multiple-query scenarios through covariance augmentation".
- Michael Sleeman, 2020, "Goal-oriented model reduction for time-dependent nonlinear parametrized partial differential equations".
- Eugene Du, 2020, "A model reduction framework with the empirical quadrature procedure for high-dimensional shape-parameterized partial differential equations".
- Keishi Kumashiro, 2019, "A physics-constrained three-dimensional three-component particle-based velocimetry method for constant-density flows".
- Keyi Ni, 2018, "An adaptive hybridizable discontinuous Petrov-Galerkin method with selective stabilization".
- Geoff Donoghue, 2018, "Reliable uncertainty quantification using adaptive stochastic discontinuous Galerkin methods".
Undergraduate thesis students
- William Sinnatamby, 2022, "Efficient method for robust topology optimization through linear decomposition".
- Coco Huang, 2022, "A comparison of selected intrusive and non-intrusive model reduction methods for parameterized linear partial differential equations".
- Jerry Bai, 2022, "Goal-oriented error estimation and adaptation for ODEs with applications to unsteady flow".
- Alireza Razavi, 2021, "Development and assessment of stabilized hyperreduction methods for nonlinear conservation laws".
- Adrian Humphry, 2020, "Multi-level Monte Carlo methods for rapid uncertainty quantification".
- Zekun Jia, 2020, "Data assimilation for engineering systems".
- Justin Lin, 2019, "Output-based error estimation and adaptation for ODEs with applications to orbit propagation".
- Sabet Seraj, 2019, "Nonlinear model reduction for problems with discontinuities".
- Eugene Du, 2018, "Uncertainty quantification with aerodynamic flows".
Undergraduate summer students
- Shiqi Xu, 2022
- Eric Dai, 2022
- William Jin, 2021
- Yewon Lee, 2019
- Alireza Razavi, 2019
- Hayden Lau, 2018
- Justin Lin, 2018