56 citations · 121 across the 14 of their papers we have counts for
14 papers
Miniaturization-Oriented Design of Spline-Parameterized UWB Antenna for In-Door Positioning Applications
Adrian Bekasiewicz, Tom Dhaene, Ivo Couckuyt +1
Design of ultra-wideband antennas for in-door localization applications is a challenging task that involves development of geometry that ensures appropriate balance between the siz…
Specification-Oriented Automatic Design of Topologically Agnostic Antenna Structure
Adrian Bekasiewicz, Mariusz Dzwonkowski, Tom Dhaene +1
Design of antennas for modern applications is a challenging task that combines cognition-driven development of topology intertwined with tuning of its parameters using rigorous num…
Strategies for Feature-Assisted Development of Topology Agnostic Planar Antennas Using Variable-Fidelity Models
Adrian Bekasiewicz, Khadijeh Askaripour, Mariusz Dzwonkowski +2
Design of antennas for contemporary applications presents a complex challenge that integrates cognitive-driven topology development with the meticulous adjustment of parameters thr…
Bayesian Optimization for Non-Convex Two-Stage Stochastic Optimization Problems
Jack M. Buckingham, Ivo Couckuyt, Juergen Branke
Bayesian optimization is a sample-efficient method for solving expensive, black-box optimization problems. Stochastic programming concerns optimization under uncertainty where, typ…
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO
Arash Heidari, Sebastian Rojas Gonzalez, Tom Dhaene +1
Multi-objective optimization is a widely studied problem in diverse fields, such as engineering and finance, that seeks to identify a set of non-dominated solutions that provide op…
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
Victor Picheny, Joel Berkeley, Henry B. Moss +13
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables th…
ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization
Jixiang Qing, Henry B. Moss, Tom Dhaene +1
We present Parallel Feasible Pareto Frontier Entropy Search (ES) -- a novel information-theoretic acquisition function for multi-objective Bayesian optimization su…
A Robust Multi-Objective Bayesian Optimization Framework Considering Input Uncertainty
J. Qing, I. Couckuyt, T. Dhaene
Bayesian optimization is a popular tool for data-efficient optimization of expensive objective functions. In real-life applications like engineering design, the designer often want…
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems
Utku Ozbulak, Baptist Vandersmissen, Azarakhsh Jalalvand +3
Given their substantial success in addressing a wide range of computer vision challenges, Convolutional Neural Networks (CNNs) are increasingly being used in smart home application…
GPflowOpt: A Bayesian Optimization Library using TensorFlow
Nicolas Knudde, Joachim van der Herten, Tom Dhaene +1
A novel Python framework for Bayesian optimization known as GPflowOpt is introduced. The package is based on the popular GPflow library for Gaussian processes, leveraging the benef…
Sensitivity Analysis of Expensive Black-Box Systems Using Metamodeling
Tom Van Steenkiste, Joachim van der Herten, Ivo Couckuyt +1
Simulations are becoming ever more common as a tool for designing complex products. Sensitivity analysis techniques can be applied to these simulations to gain insight, or to reduc…
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes
Joachim van der Herten, Ivo Couckuyt, Tom Dhaene
Student- processes have recently been proposed as an appealing alternative non-parameteric function prior. They feature enhanced flexibility and predictive variance. In this wor…
Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty
Joachim van der Herten, Ivo Couckuyt, Dirk Deschrijver +1
When approximating a black-box function, sampling with active learning focussing on regions with non-linear responses tends to improve accuracy. We present the FLOLA-Voronoi method…
Fast Calculation of the Knowledge Gradient for Optimization of Deterministic Engineering Simulations
Joachim van der Herten, Ivo Couckuyt, Dirk Deschrijver +1
A novel efficient method for computing the Knowledge-Gradient policy for Continuous Parameters (KGCP) for deterministic optimization is derived. The differences with Expected Impro…