Fall 2019 – Mechanical Engineering Career Fair
Monday, September 16, 2019 @ 10 a.m. – 4 p.m.
Dust off your resume, and join us for the ME Career Fair to learn more about Mechanical Engineering internships and job opportunities!
Participating companies include:
Procter & Gamble
Hosted by: Pi Tau Sigma
Deep Learning Methodologies and Tools for Scientific Problems
Monday, September 16, 2019 @ 12:00 p.m.
3110 Etcheverry Hall
Chiyu “Max” Jiang – Ph.D. Student, Department of Mechanical Engineering, University of California, Berkeley
Berkeley Fluids Seminar
Abstract: Modern tools in machine learning and deep learning can offer new insights to old problems, both on the methodological side as well as on the technical side. The first half of this talk will present an overview of tools and subfields in geo¬metric deep learning that are particularly relevant and applicable to physical problems, (e.g., learning on regular grids, meshes/graphs and point clouds, equivalent representations using spherical harmonics etc.), including some of Jiang’s recent work in this area. Specifically for a fluids audience, Jiang will also discuss work that uses deep learning methodologies towards fluids applications. The second half of the talk will discuss various computational tools that have been developed in the deep learning realm including GPU-based frameworks (e.g., PyTorch, Tensorflow) that have various unique properties (python friendly, GPU ready, high performance, built-in differentiability). Such frameworks offer the possibility of writing high-performance linear algebra code with a high level user friendly numpy-like programming interface, that can execute computation agnostic to the specific hardware (CPU, GPU, parallel computation on a cluster). Though designed with deep learning applications in mind, these computational tools might appeal to a wider audience in other computational fields for its flexibility and high performance.
Biography: Chiyu “Max” Jiang is a 5th-Year PhD student in Mechanical Engineering advised by Dr. Philip Marcus. He is currently interning in the Perception Team at Google AI, and is affiliated with the data analytics group at Lawrence Berkeley National Lab. His research interest is in 3-dimensional machine learning / deep learning, and its applications ranging from computer vision to physics and climate science.
Hosted by: Professor Philip S. Marcus, 6121 Etcheverry Hall, (510) 642-5942, firstname.lastname@example.org & Associate Professor M. Reza Alam, 6111 Etcheverry Hall, (510) 643-2591, email@example.com
Directional Energy Transfers in a Deep-Water, Extreme Ocean Wave
Thursday, September 19, 2019 @ 12:00 p.m.
3110 Etcheverry Hall
Dylan Barratt – DPhil Student, Department of Engineering Science; University of Oxford, UK
Abstract: Steep, focusing waves can experience fast and local nonlinear evolution of the spectrum due to wave-wave interactions resulting in energy transfer to both higher and lower wavenumber components. The shape and kinematics of a steep wave may, thus, differ substantially from the predictions of linear theory. We have investigated the role of nonlinear interactions on group shape for a steep, narrow-banded, directionally-spread wave group focusing in deep water using the fully-nonlinear potential flow solver, OceanWave3D. The initially narrow-banded amplitude spectrum exhibits the formation of sidelobes at angles of approximately 35 degrees to the spectral peak during the simulated extreme wave event, occurring in approximately 10 wave periods, with a preferential energy transfer to high-wavenumber components. The directional energy transfer is attributed to resonant third-order interactions with a qualitative resemblance to the resonant interactions of a degenerate quartet. The rate of growth of resonant components has also been calculated in terms of wave action density and found to agree well with the faster ‘dynamical’ time-scale associated with nearly-resonant interactions and the Zakharov equation rather than the slower ‘kinetic’ time-scale associated with exactly-resonant interactions and the kinetic wave equation. The engineering implications of energy transfer to oblique high-wavenumber components during an extreme wave event are also discussed.
Biography: Dylan Barratt is currently completing a DPhil degree (10/2017-present) in the Department of Engineering Science at the University of Oxford, UK, with a EPSRC Studentship from the UK government. His thesis focuses on the local and global features of resonant third-order interactions amongst surface gravity waves and relies upon fully-nonlinear potential flow simulations as a means of analysis. Before arriving at Oxford, he was employed as a scientific assistant at ETH Zurich, Switzerland, in the Institute for Energy Technology (07/2015-09/2017) where he designed and built a model wind turbine test facility to measure the unsteady aerodynamic loads of a floating offshore wind turbine in collaboration with Hitachi Ltd. He obtained an MSc in mechanical engineering from Seoul National University, Korea, where he completed a thesis on the internal cooling of turbine blades as a member of the Turbomachinery Laboratory (09/2013-07/2015) with sponsorship from the Oppenheimer Memorial Trust of South Africa. He also holds a BSc in mechanical engineering (01/2009-12/2012) from the University of the Witwatersrand, South Africa, receiving the William John Walker Gold Medal as the top graduate in the School of Mechanical, Industrial and Aeronautical Engineering.
Hosted by: Associate Professor M. Reza Alam, 6111 Etcheverry Hall, (510) 643-2591, firstname.lastname@example.org