60 research outputs found
Aerodynamic Design Exploration through Surrogate-Assisted Illumination
International audienceA new method for design space exploration and optimization, Surrogate-Assisted Illumination (SAIL), is presented. Inspired by robotics techniques designed to produce diverse repertoires of behaviors for use in damage recovery, SAIL produces diverse designs that vary according to features specified by the designer. By producing high-performing designs with varied combinations of user-defined features a map of the design space is created. This map illuminates the relationship between the chosen features and performance, and can aid designers in identifying promising design concepts. SAIL is designed for use with compu-tationally expensive design problems, such as fluid or structural dynamics, and integrates approximative models and intelligent sampling of the objective function to minimize the number of function evaluations required. On a 2D airfoil optimization problem SAIL is shown to produce hundreds of diverse designs which perform competitively with those found by state-of-the-art black box optimization. Its capabilities are further illustrated in a more expensive 3D aerodynamic optimization task
Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-optimization
Quality diversity algorithms can be used to efficiently create a diverse set
of solutions to inform engineers' intuition. But quality diversity is not
efficient in very expensive problems, needing 100.000s of evaluations. Even
with the assistance of surrogate models, quality diversity needs 100s or even
1000s of evaluations, which can make it use infeasible. In this study we try to
tackle this problem by using a pre-optimization strategy on a lower-dimensional
optimization problem and then map the solutions to a higher-dimensional case.
For a use case to design buildings that minimize wind nuisance, we show that we
can predict flow features around 3D buildings from 2D flow features around
building footprints. For a diverse set of building designs, by sampling the
space of 2D footprints with a quality diversity algorithm, a predictive model
can be trained that is more accurate than when trained on a set of footprints
that were selected with a space-filling algorithm like the Sobol sequence.
Simulating only 16 buildings in 3D, a set of 1024 building designs with low
predicted wind nuisance is created. We show that we can produce better machine
learning models by producing training data with quality diversity instead of
using common sampling techniques. The method can bootstrap generative design in
a computationally expensive 3D domain and allow engineers to sweep the design
space, understanding wind nuisance in early design phases.Comment: This is the final version and has been accepted for publication in
Evolutionary Computation (MIT Press
Effiziente Identifikation parametrisierter Kreislaufmodelle
In der vorliegenden Arbeit werden Verfahren vorgestellt, die geeignet sind, Modelle des menschlichen kardiovaskulären Systems an individuelle Kreislaufreaktionen anzupassen. Allgemeine Kreislaufmodelle des menschlichen kardiovaskulären Systems sind in der Regel nichtlineare Differentialgleichungssysteme, für die es keine effizienten Optimierungsverfahren gibt. Durch die Einschränkung auf relevante Aspekte (bzgl. der Individualisierungsaufgabe) wird ein solches Modell auf Modelle einfacherer Struktur projiziert, die eine Approximation durch Funktionsapproximatoren erlauben, für welche wiederum effiziente Optimierungsalgorithmen existieren. In Abhängigkeit von der Struktur der Individualisierungsaufgabe kommt zusätzlich ein modifiziertes BFGS-Verfahren zum Einsatz, das Approximationen solcher Modellaspekte verwendet um die Konvergenz der Modellindividualisierung zu verbessern
Evolution of optimal control for energy-efficient transport
An evolutionary algorithm is presented to solve the optimal control problem for energy optimal driving. Results show that the algorithm computes equivalent strategies as traditional graph searching approaches like dynamic programming or A*. The algorithm proves to be time efficient while saving multiple orders of magnitude in memory compared to graph searching techniques. Thereby making it applicable in embedded applications such as eco-driving assistants or intelligent route planning
Predicting Performance from Outdoor Cycling Training with the Fitness-Fatigue Model
The Fitness-Fatigue model (Calvert et al. 1976) is widely used for performance analysis. This antagonistic model is based on a fitness-term, a fatigue-term, and an initial basic level of performance. Instead of generic parameter values, individualizing the model needs a fitting of parameters. With fitted parameters, the model adapts to account for individual responses to strain. Even though in most cases fitting of recorded training data shows useful results, without modification the model cannot be simply used for prediction
Evolving look ahead controllers for energy optimal driving and path planning
An evolved neural network controller is presented to solve the optimal control problem for energy optimal driving. A controller is produced which computes equivalent control commands to traditional graph searching approaches, while able to adapt to varied constraints and conditions. Furthermore, after training, trivial amounts of computation time and memory are required, making the approach applicable for embedded systems and path planning applications
Theoretische Informatik: eine Einführung in Berechenbarkeit, Komplexität und formale Sprachen mit 101 Beispielen
Eine anschauliche Einführung in die klassischen Themenbereiche der Theoretischen Informatik für Studierende der Informatik im Haupt- und Nebenfach. Die Autoren wählen einen Ansatz, der durch zahlreiche ausgearbeitete Beispiele auch LeserInnen mit nur elementaren Mathematikkenntnissen den Zugang zu Berechenbarkeit, Komplexitätstheorie und formalen Sprachen ermöglicht. Die mathematischen Konzepte werden sowohl formal eingeführt als auch informell erläutert und durch grafische Darstellungen veranschaulicht. Das Buch umfasst den Lehrstoff einführender Vorlesungen in die Theoretische Informatik und bietet zahlreiche Übungsaufgaben zu jedem Kapitel an. (Verlagsangaben
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