6 results on '"GORISSEN, DIRK"'
Search Results
2. Sequential modeling of a low noise amplifier with neural networks and active learning
- Author
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Gorissen, Dirk, De Tommasi, Luciano, Crombecq, Karel, and Dhaene, Tom
- Published
- 2009
- Full Text
- View/download PDF
3. A surrogate modeling and adaptive sampling toolbox for computer based design
- Author
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Gorissen, Dirk, Couckuyt, Ivo, Demeester, Piet, Dhaene, Tom, and Crombecq, Karel
- Subjects
Computer. Automation ,model selection ,distributed computing ,Technology and Engineering ,active learning ,metamodeling ,function approximation ,adaptive sampling ,surrogate modeling - Abstract
An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging design problems. However, due to the computational cost of these high fidelity simulations, the use of neural networks, kernel methods, and other surrogate modeling techniques have become indispensable. Surrogate models are compact and cheap to evaluate, and have proven very useful for tasks such as optimization, design space exploration, prototyping, and sensitivity analysis. Consequently, in many fields there is great interest in tools and techniques that facilitate the construction of such regression models, while minimizing the computational cost and maximizing model accuracy. This paper presents a mature, flexible, and adaptive machine learning toolkit for regression modeling and active learning to tackle these issues. The toolkit brings together algorithms for data fitting, model selection, sample selection (active learning), hyperparameter optimization, and distributed computing in order to empower a domain expert to efficiently generate an accurate model for the problem or data at hand.
- Published
- 2010
4. A NOVEL HYBRID SEQUENTIAL DESIGN STRATEGY FOR GLOBAL SURROGATE MODELING OF COMPUTER EXPERIMENTS.
- Author
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CROMBECQ, KAREL, GORISSEN, DIRK, DESCHRIJVER, DIRK, and DHAENE, TOM
- Subjects
- *
COMPUTER simulation , *MONTE Carlo method , *MATHEMATICAL models , *NUMERICAL analysis , *MATHEMATICAL analysis - Abstract
Many complex real-world systems can be accurately modeled by simulations. However, high-fidelity simulations may take hours or even days to compute. Because this can be impractical, a surrogate model is often used to approximate the dynamic behavior of the original simulator. This model can then be used as a cheap, drop-in replacement for the simulator. Because simulations can be very expensive, the data points, which are required to build the model, must be chosen as optimally as possible. Sequential design strategies offer a huge advantage over one-shot experimental designs because they can use information gathered from previous data points in order to determine I lie location of new data points. Each sequential design strategy must perform a trade-off between exploration and exploitation, where the former involves selecting data points in unexplored regions of the design space, while the latter suggests adding data points in regions which were previously identified to be interesting (for example, highly nonlinear regions). In this paper, a novel hybrid sequential design strategy is proposed which uses a Monte Carlo-based approximation of a Voronoi tessellation for exploration and local linear approximations of the simulator for exploitation. The advantage of this method over other sequential design methods is that it is independent of the model type, and can therefore be used in heterogeneous modeling environments, where multiple model types are used at the same time. The new method is demonstrated on a number of test problems, showing that it is a robust, competitive, and efficient sequential design strategy. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
5. Evolutionary Model Type Selection for Global Surrogate Modeling.
- Author
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Gorissen, Dirk, Dhaene, Tom, and De Turck, Filip
- Subjects
- *
SURROGATE-based optimization , *COMPUTATIONAL complexity , *APPROXIMATION theory , *GENETIC algorithms , *RANKING (Statistics) , *TOPOLOGICAL spaces , *ADAPTIVE sampling (Statistics) , *ITERATIVE methods (Mathematics) - Abstract
Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist (Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm (heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type. [ABSTRACT FROM AUTHOR]
- Published
- 2009
6. Evolutionary model type selection for global surrogate modeling
- Author
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Gorissen, Dirk, Tom Dhaene, and Filip De Turck
- Subjects
Technology and Engineering ,CONTINUOUS SPACE ,model type selection ,ALGORITHMS ,INFINITE POPULATION-SIZE ,global surrogate modeling ,function approximation ,ENGINEERING DESIGN ,adaptive sampling ,genetic algorithms ,THEORETICAL-ANALYSIS ,active learning ,SIMULATION ,NEURAL-NETWORKS ,METAMODELING TECHNIQUES ,OPTIMIZATION ,APPROXIMATION - Abstract
Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist ( Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm ( heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type.
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