Searched for: subject%3A%22symbolic%255C+regression%22
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Derner, Erik (author), Kubalík, Jiří (author), Ancona, N. (author), Babuska, R. (author)
Developing mathematical models of dynamic systems is central to many disciplines of engineering and science. Models facilitate simulations, analysis of the system's behavior, decision making and design of automatic control algorithms. Even inherently model-free control techniques such as reinforcement learning (RL) have been shown to benefit...
journal article 2020
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Kubalik, Jiai (author), Derner, Erik (author), Babuska, R. (author)
In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do not sufficiently cover the input space, the prediction error does not provide sufficient guidance toward desired models. Standard symbolic regression techniques then...
conference paper 2020
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Kramer, O.J.I. (author), El Hasadi, Yousef M.F. (author), de Moel, P.J. (author), Baars, Eric T. (author), Padding, J.T. (author), van der Hoek, J.P. (author)
For an accurate prediction of the porosity of a liquid-solid homogenous fluidized bed, various empirical prediction models have been developed. Symbolic regression machine learning techniques are suitable for analyzing experimental fluidization data to produce empirical expressions for porosity as a function not only of fluid velocity and...
conference paper 2019
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Schmelzer, M. (author), Dwight, R.P. (author), Cinnella, Paola (author)
A novel deterministic symbolic regression method SpaRTA (Sparse Regression of Turbulent Stress Anisotropy) is introduced to infer algebraic stress models for the closure of RANS equations directly from high-fidelity LES or DNS data. The models are written as tensor polynomials and are built from a library of candidate functions. The machine...
journal article 2019
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Märtens, M. (author)
This thesis is a contribution to a deeper understanding of how information propagates and what this process entails. At its very core is the concept of the network: a collection of nodes and links, which describes the structure of the systems under investigation. The network is a mathematical model which allows to focus on a very fundamental...
doctoral thesis 2018
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Märtens, M. (author), Kuipers, F.A. (author), Van Mieghem, P.F.A. (author)
Networks are continuously growing in complexity, which creates challenges for determining their most important characteristics. While analytical bounds are often too conservative, the computational effort of algorithmic approaches does not scale well with network size. This work uses Cartesian Genetic Programming for symbolic regression to...
conference paper 2017
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Kubalík, Jiří (author), Alibekov, Eduard (author), Babuska, R. (author)
Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value function and policy mappings. This paper addresses the problem of finding a smooth policy...
journal article 2017
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Thorat, R.R. (author)
This thesis was performed in the framework of ErasmusMundus EU-INDIA scholarship programme. The main goal is to elucidate particle enhanced foam flow (surfactant water and nitrogen gas) in porous media near the critical micelle concentration. The thesis is divided in four parts: in the first part the modeling of foam flow is investigated, in the...
doctoral thesis 2016
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Kubalìk, Jiřì (author), Alibekov, Eduard (author), Žegklitz, Jan (author), Babuska, R. (author)
This paper presents a first step of our research on designing an effective and efficient GP-based method for symbolic regression. First, we propose three extensions of the standard Single Node GP, namely (1) a selection strategy for choosing nodes to be mutated based on depth and performance of the nodes, (2) operators for placing a compact...
conference paper 2016
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