Jürgen Branke is Professor of Operational Research and Systems at the University of Warwick, Coventry, U.K. He has been an active researcher in the area of evolutionary optimization since 1994 and has published over 180 papers in international peer-reviewed journals and conferences. His current research interests include Bayesian optimization, multiobjective optimization, handling of uncertainty in optimization, dynamically changing optimization problems, simulation.based optimization, and the design of complex systems. Prof. Branke is Editor-in-Chief of the ACM Transactions on Evolutionary Learning and Optimization, Area Editor of the Journal of Heuristics and the Journal on Multi-Criteria Decision Analysis, as well as an Associate Editor of the IEEE Transactions in Evolutionary Computation and the Evolutionary Computation Journal.
Abstract
Bayesian optimisation is a relatively new but powerful technique for computationally expensive black-box optimisation. It builds a surrogate model of the fitness landscape, and then uses this model to iteratively decide which additional designs are most promising to evaluate. This talk will give a brief introduction to Bayesian optimisation and then focuses on recent developments that allow to use Bayesian optimisation for a) multi-concept design, where one has to select among different design concepts, each with different design variables, and b) multi-task problems, where solutions for several related problems have to be identified.
Dr. Keith Meintjes has over 35 years of experience in the development and application of simulation tools to transform product development. His achievements include novel methods for combustion simulation, patents for engine design, and strategic planning for the world’s largest commercial HPC facility.
Dr. Meintjes joined CIMdata when they acquired CPDA, where he was Research Director for CAE and managed CPDA’s Design/Simulation Council. Previously, he spent nearly 30 years at General Motors.
At GM Dr. Meintjes was responsible for the engineering requirements for GM’s Global CAE IT infrastructure and was named Senior Technical Fellow. At GM Powertrain he held strategic, planning, and management positions, to embed simulation tools in the powertrain product development process. Earlier, at the GM Research Laboratories, Dr. Meintjes developed thermodynamic and CFD simulation models for engine performance and combustion, and was instrumental in GM’s acquisition of a Cray supercomputer.
Dr. Meintjes holds BSc and MSc degrees in Mechanical Engineering, with a specialty in Aeronautics, from the University of the Witwatersrand, and an MA and Ph.D. from Princeton University.
Abstract
Ideas for what we now call Generative Design, including topology optimization, were developed in the 1980s but did not fully take root. The evolution of additive manufacturing (3D printing) has sparked new interest, helping users to produce objects not manufacturable by traditional methods. Manufacturing is, however, only part of the story. There are several enabling technologies that come into play.
Generative Design is, by its nature, an optimization, and is almost always supported by physics-based simulation to evaluate the design as it evolves. Statements of requirements and constraints are used to create one or more feasible designs. This contrasts with the traditional process, where a design must be created so it can be assessed. This assessment often leads to an iterative cycle of redesign and reassessment, until the product performance requirements are met.
We are evolving to Generative Engineering (or Human-Assisted Design) by leveraging the astounding improvements in computer and software capability to create an environment that will revolutionize not only product engineering and development, but the entire lifecycle that PLM oversees. This presentation will provide a vision for how technologies including Generative Design, Simulation & Analysis, Big Data Analytics, Advanced Materials, and Robust Design will converge to an Augmented Intelligence environment that will dramatically change product development over the PLM lifecycle.
The issue is not simply one of technology. Corporate process, culture and people play a very important role. We will discuss suggestions for how to implement a governance process to enable learning about Generative Design, and then deploying it with maximum effect.
Pranay Seshadri is the Aeronautics Group Leader in the Data-Centric Engineering Programme at the Alan Turing Institute. He is also a Postdoctoral Fellow at the Department of Engineering in the University of Cambridge. He obtained his PhD in Computational Engineering in 2016 from the University of Cambridge.
Abstract
Techniques for subspace-based dimension reduction are starting to make their way into engineering workflows. They facilitate powerful inference tailored for both design and manufacturing tasks by identifying linear combinations of parameters that are important with respect to key output quantities of interest. The central idea is to use these methods for empowering more efficient (and faster) optimisation, uncertainty quantification and sensitivity analysis studies, by drastically reducing the number of model evaluations. In this talk, I will provide an overview of such techniques with an exposition of the formulas and code. I will also provide a few industrial examples of how subspace-based dimension reduction can be used to solve salient problems in aeronautics.
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