Searched for: subject%3A%22symbolic%255C%2Bregression%22
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Hoefnagel, Kaj (author)
Computational fluid dynamics (CFD) is an important tool in design involving fluid flow. Scale-resolving CFD methods exist, but they are too computationally expensive for practical design. Instead, the relatively cheap Reynolds-averaged Navier-Stokes (RANS) approach is the industry standard, specifically models based on the Boussinesq hypothesis,...
master thesis 2023
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Thamban, Arun (author)
From the motion of electrons in an atom to the orbits of celestial bodies in the cosmos, governing equations are essential to the characterisation of dynamical systems. They facilitate an understanding of the physics of a system, which enables the development of useful techniques such as predictive control. An increasingly popular method to...
master thesis 2023
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Kubalik, Jiri (author), Derner, Erik (author), Babuska, R. (author)
Many real-world systems can be described by mathematical models that are human-comprehensible, easy to analyze and help explain the system's behavior. Symbolic regression is a method that can automatically generate such models from data. Historically, symbolic regression has been predominantly realized by genetic programming, a method that...
journal article 2023
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Harrison, Joe (author), Virgolin, Marco (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
The aim of Symbolic Regression (SR) is to discover interpretable expressions that accurately describe data. The accuracy of an expression depends on both its structure and coefficients. To keep the structure simple enough to be interpretable, effective coefficient optimisation becomes key. Gradient-based optimisation is clearly effective at...
conference paper 2023
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van Leeuwen, Elske (author)
Computational Fluid Dynamics (CFD) is the main tool to use in industry and engineering problems including turbulent flows. Turbulence modeling relies on solving the Navier-Stokes equations. Solving these equations directly takes a lot of time and computational power. More affordable methods solve the Reynolds Averaged Navier-Stokes (RANS)...
master thesis 2022
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Hemmes, Jasper (author)
When simulating fluids the industry standard is Reynolds averaged Navier-Stokes (RANS). However, the results for certain flows are inaccurate. The main source of error in popular RANS turbulence models is the Boussinesq approximation, assuming a linear relationship between the Reynolds stress anisotropy and the mean rate of strain. Experiments...
master thesis 2022
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Zhou, H. (author)
Applying deep neural networks (DNNs) for system identification (SYSID) has attracted more andmore attention in recent years. The DNNs, which have universal approximation capabilities for any measurable function, have been successfully implemented in SYSID tasks with typical network structures, e.g., feed-forward neural networks and recurrent...
doctoral thesis 2022
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Liu, D. (author), Virgolin, Marco (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Genetic programming (GP) is one of the best approaches today to discover symbolic regression models. To find models that trade off accuracy and complexity, the non-dominated sorting genetic algorithm II (NSGA-II) is widely used. Unfortunately, it has been shown that NSGA-II can be inefficient: in early generations, low-complexity models over...
conference paper 2022
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Harrison, J. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Genetic Programming (GP) can make an important contribution to explainable artificial intelligence because it can create symbolic expressions as machine learning models. Nevertheless, to be explainable, the expressions must not become too large. This may, however, limit their potential to be accurate. The re-use of subexpressions has the...
conference paper 2022
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Kramer, O.J.I. (author)
In drinking water treatment plants, multiphase flows are a frequent phenomenon. Examples of such flows are pellet-softening and filter backwashing where liquid-solid fluidisation is applied. A better grasp of these fluidisation processes is needed to be able to determine optimal hydraulic states. In this research, models were developed, and...
doctoral thesis 2021
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Kubalik, Jiri (author), Derner, Erik (author), Zegklitz, Jan (author), Babuska, R. (author)
Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as neural networks or basis...
journal article 2021
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Huijing, Jasper P. (author), Dwight, R.P. (author), Schmelzer, M. (author)
In this short note we apply the recently proposed data-driven RANS closure modelling framework of Schmelzer et al.(2020) to fully three-dimensional, high Reynolds number flows: namely wall-mounted cubes and cuboids at Re=40,000, and a cylinder at Re=140,000. For each flow, a new RANS closure is generated using sparse symbolic regression based...
journal article 2021
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Kubalík, Jiří (author), Derner, Erik (author), Babuska, R. (author)
Virtually all dynamic system control methods benefit from the availability of an accurate mathematical model of the system. This includes also methods like reinforcement learning, which can be vastly sped up and made safer by using a dynamic system model. However, obtaining a sufficient amount of informative data for constructing dynamic...
journal article 2021
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Derner, Erik (author), Kubalik, Jiri (author), Babuska, R. (author)
Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the model accuracy is impaired when data from repetitive motions prevail in the training set and outweigh...
journal article 2021
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Lingmont, Hidde (author)
The advent of machine learning and the availability of big data brought a novel approach for researchers to discover fundamental laws of motion. Computers allow to quickly find underlying physical laws from experimental data, without having in-depth knowledge of the system. Applications are widespread among numerous fields such as physics,...
master thesis 2020
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Goderie, M.W. (author)
Wind turbine wakes cause significant reductions in power production and increased fatigue damage for downwind turbines. Thus, they affect the wind levelized cost of energy. Computational Fluid Dynamics (CFD) can be used to quantify the wake characteristics, whereby Reynolds-averaged Navier-Stokes (RANS) has the most potential for industrial...
master thesis 2020
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Nieuwenhuisen, Kyle (author)
Adverse pressure gradients, separation and other forms of non-equilibrium flows are often encountered in flows of interest. In these type of flows, the Boussinesq hypothesis does not hold and often leads to erroneous predictions by eddy viscosity models. In an attempt to capture these non-equilibrium effects, lag parameter models introduce a lag...
master thesis 2020
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Schmelzer, M. (author), Dwight, R.P. (author), Cinnella, Paola (author)
In this work recent advancements are presented in utilising deterministic symbolic regression to infer algebraic models for turbulent stress-strain relation with sparsity-promoting regression techniques. The goal is to build a functional expression from a set of candidate functions in order to represent the target data most accurately....
conference paper 2020
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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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