I’m thrilled to have been named MIT’s next vice chancellor for undergraduate and graduate education, effective Feb. 17. Here’s an MIT News article with more info. I am also very grateful for the support from many around the AeroAstro department, the Offices of the Chancellor and Vice Chancellor, and throughout MIT during this transition.
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Vice chancellor
I'm thrilled to have been named MIT’s next vice chancellor for undergraduate …
Amanda Medina presents at SciTech!
Amanda Medina, a UROP in our group, presented her first-author paper "Wall-Model …
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Amanda Medina, a UROP in our group, presented her first-author paper “Wall-Model Effects on RANS for a Fully Developed Channel Flow” at the AIAA SciTech meeting on January 9, 2025. One of the interesting results in her paper shows that a typical equilibrium wall model approach using the velocity off the wall to determine the skin friction converges at first order with mesh resolution.
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Lucien Rochery, a post doc in our group, presented his paper “Metris: An Open-Source High-Order Metric-Based Remesher” describing our new open-source meshing software at the AIAA SciTech meeting in January. Mark Chiriac, a UROP in our group, was a co-author that developed the optimization algorithm to curve the mesh to better match the metric field.
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Emily uploaded a new paper to arXiv on physics-informed PDE discretization methods. The paper is co-authored with her DOE collaborators Amanda Howard, Brek Meuris, Panos Stinis from the Pacific Northwest National Laboratory. For more on all of Emily’s work, check out her site.
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Cory published another paper from his doctoral research, this one appears in the Journal of Computational Physics and describes a framework for error modeling and identification using small-sample Bayesian inference that allows approximation of the optimal balance between sampling time and discretization precision without the computation of high-cost libraries of reference solutions. The result enables the possibility of running chaotic and turbulent simulations in a way that minimizes the total error between sampling and discretization without prior knowledge of the error behavior of the system.
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Emily presented two talks on her work “Machine-Learning-Based Spectral Methods Toward Reduced-Order Modeling for Partial Differential Equations”. One at the Computational Research in Boston and Beyond Seminars (October 4) and the other at the MIT Center for Computational Science & Engineering Community Seminars (October 11). The work is a collaboration with her DOE collaborators Amanda Howard, Brek Meuris, Panos Stinis from the Pacific Northwest National Laboratory.
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Renato Trono Figueras presented our paper “Improvements to an Output-Based Adaptive, Higher-Order Finite Element Method for Sonic Boom Propagation” at the 2024 AIAA Aviation. This work shows how space-time adaptive finite element methods can be used to calculate sonic boom propagation and is based upon the Renato’s SM thesis.
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Emily Williams presented “Learning Diffusion for Stabilizing Linearized Chaotic Systems” at the 2024 SIAM Annual Meeting. This was work done in collaboration with another group member, Renato Trono Figueras. They show how machine learning can be used to develop a stochastic diffusion model for linearized chaotic systems.
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Thank you to Dan Huttonlocher (Dean of the Schwarzman College of Computing (SCC)) for the recognition of my teaching of Introduction to Computational Science & Engineering subject (see the background here). This class was developed with many collaborators across the MIT Center for Computational Science & Engineering (CCSE). With support from the Common Ground for Computing Education, the Department of Aeronautics & Astronautics, the Department of Mathematics, CCSE, and SCC, Prof. Laurent Demanet and I were the main developers and original instructors of this subject which was first offered in Fall of 2020. More recently, Prof. Nidhi Seethapathi and I have been co-teaching it. Throughout the original development to 4+ years of teaching it, this highly collaborative, multi-department effort has been a highlight of my academic career.
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Carmen published a paper on her doctoral research on wall-modeling in the AIAA Journal. Unlike typical wall models, Carmen’s approach avoids the need to query the interior solution by utilizing solution information solely at the boundary, making it well-suited for unstructured grids and mesh adaptation. Furthermore, it is formulated to lessen the near-wall grid resolution requirements by, below the log layer, making the eddy viscosity approach a constant, nonzero value and the velocity varying approximately linearly with distance from the wall. The image shows the lift vs angle of attack for a Joukowski airfoil comparing the WMRANS to RANS showing excellent ability to match RANS even at stall. This past summer, Carmen presented the model at the AIAA Aviation conference. And, here’s a link to her dissertation which also includes results for WMLES and DES.