*时间: 2020年9月30日晚上21: 00
*地点: 腾讯ID: 381 208 740 http://meeting.tencent.com/s/wFIJf9TzhUYp
Dr. Mohebujjaman obtained his Ph.D. in 2017 from Clemson University, USA. He then worked as a Visiting Assistant Professor in the Department of Mathematics at Virginia Tech, Blacksburg, VA. In 2019, he started working as a Post-doctoral Associate at Massachusetts Institute of Technology (MIT), Cambridge, MA. He played there an active role in the modeling group for building a high-temperature superconducting magnet for the new generation tokamak SPARC. From fall 2020, he is working as an Assistant Professor in the Department of Mathematics and Physics at Texas A&M International University. He is interested in numerical analysis/methods, large scale simulation of fluid flow problems including Newtonian Navier-Stokes equations, Magnetohydrodynamics (MHD), Uncertainty Quantification (UQ), fast algorithms, reduced order modeling (ROM), and large scale parallel simulation of Maxwell equations with multi-billions degrees of freedom.
In this talk, at first, we present two approaches for enforcing better conservation properties for reduced-order models (ROMs) of fluid flows. In the first approach, to construct the centering trajectory, we use the Stokes extension instead of the standard snapshot average. We show that the Stokes extension yields significantly more accurate results. In the second approach, we enforce physical constraints in the data-driven modeling of the ROM closure term. The constrained data-driven ROM is significantly more accurate than its unconstrained counterpart.
Finally, we propose a novel high-order ROM differential filter and use it in conjunction with an evolve-filter-relax algorithm to attenuate the numerical oscillations of standard ROMs. We also examine how stochastic collocation methods can be combined with the EFR algorithm for efficient UQ of fluid flows.
1. An evolve-filter-relax stabilized reduced order stochastic collocation method for the time-dependent Navier-Stokes Equations, M. Gunzburger, T. Iliescu, M. Mohebujjaman, and M. Schneier, SIAM/ASA Journal on Uncertainty Quantification, 7(4), 1162-1184, 2019.
2. Physically-Constrained Data-Driven Correction for Reduced Order Modeling of Fluid Flows, M. Mohebujjaman, L. G. Rebholz, and T. Iliescu, International Journal for Numerical Methods in Fluids, 89(3), 103-122, 2019.
3. Data-Driven Filtered Reduced Order Modeling of Fluid Flows, X. Xie, M. Mohebujjaman, L.G. Rebholz, and T. Iliescu, SIAM Journal on Scientific Computing, 40(3), B834-B857, 2018.
电竞投注 4. Energy Balance and Mass Conservation in Reduced Order Models of Fluid Flows, M. Mohebujjaman, L.G. Rebholz, X. Xie, and T. Iliescu, Journal of Computational Physics, 321, 128-142, 2017.