Lecture

Design Optimization: Principles and Applications

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

In course

ME-403: Applied mechanical design

Students will be exposed to hands-on design problems throughout the term. They will acquire methodologies to (1) address open ended engineering problems, (2) cultivate creativity, (3) support decision making and (4) develop problem solving abilities. ...

Description

This lecture introduces the fundamentals of design optimization, discussing the selection of the best design within given constraints and objectives. It covers virtual prototypes, Pareto-optimality, multi-objective optimization, and challenges faced in the field. The instructor presents real-world examples and tools like MATLAB and pymoo for optimization.

Instructor

Jürg Alexander Schiffmann

Teaching & PhD PhD Students Hendrik Dietmar Schillinger, Gabriele Iovanna, Husam Jubran, Selahittin Atilay Zervent, Harshita Ravindra Agarwal, Anupam Jena, Issam Soukhmane, Tristan Wolff Schwab, Aman Jain, Masato Omotsubo, Konstantin Andreas Jacoby, Andrii Artomov, Arnau Catala I Rams, Elize Alwash, Emanuele Pallaoro, Anna Abramishvili Past EPFL PhD Students Violette Mounier, Karim Magdi Zakaria Abdelrahman Shalash, Patrick Hubert Wagner, Markus Diehl, Philipp Kaspar Bättig, Kévin Rosset, Eliott Philippe

Official source

Related concepts (31)

Multidisciplinary design optimization

Multi-disciplinary design optimization (MDO) is a field of engineering that uses optimization methods to solve design problems incorporating a number of disciplines. It is also known as multidisciplinary system design optimization (MSDO), and Multidisciplinary Design Analysis and Optimization (MDAO). MDO allows designers to incorporate all relevant disciplines simultaneously. The optimum of the simultaneous problem is superior to the design found by optimizing each discipline sequentially, since it can exploit the interactions between the disciplines.

Metaheuristic

In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored.

Memetic algorithm

A memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm (GA) or more general evolutionary algorithm (EA). It may provide a sufficiently good solution to an optimization problem. It uses a suitable heuristic or local search technique to improve the quality of solutions generated by the EA and to reduce the likelihood of premature convergence. Memetic algorithms represent one of the recent growing areas of research in evolutionary computation.

Simulated annealing

Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. For large numbers of local optima, SA can find the global optima. It is often used when the search space is discrete (for example the traveling salesman problem, the boolean satisfiability problem, protein structure prediction, and job-shop scheduling).

Global optimization

Global optimization is a branch of applied mathematics and numerical analysis that attempts to find the global minima or maxima of a function or a set of functions on a given set. It is usually described as a minimization problem because the maximization of the real-valued function is equivalent to the minimization of the function . Given a possibly nonlinear and non-convex continuous function with the global minima and the set of all global minimizers in , the standard minimization problem can be given as that is, finding and a global minimizer in ; where is a (not necessarily convex) compact set defined by inequalities .

Related lectures (64)

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Markov Chains and Algorithm Applications

Covers the application of Markov chains and algorithms for function optimization and graph colorings.

Heuristic Optimization Methods

Explores heuristic optimization methods to find the global optimum efficiently.

Computational Aspects of Optimization MOOC: Simulation Neurocience

Explores optimization in neuron modeling, addressing underconstrained parameters, fitness functions, and successful fitting of firing patterns.

Optimisation in Energy Systems