Researcher
Tom Dhaene
- Keywords:design optimization, surrogate modeling, data-efficient machine learning
- Disciplines:Machine learning and decision making, Modelling and simulation, Computer-aided design, Electromagnetism and antenna technology, Antennas and propagation, Health informatics
Affiliations
- Department of Information technology (Department)
Member
From1 Jan 1993 → Today
Projects
1 - 10 of 19
- Bayesian Active Learning for EMI Near-Field Emission Characterization of High-Speed ElectronicsFrom1 Jan 2024 → TodayFunding: FWO research project (including WEAVE projects)
- Machine Learning and Radar Sensors for Monitoring Patients and Elderly People in HealthcareFrom1 Feb 2023 → 30 Sep 2023Funding: BOF - doctoral mandates
- Generative design of linear passive electronic systemsFrom1 Jan 2019 → 31 Dec 2022Funding: FWO research project (including WEAVE projects)
- Learning complex patterns with Gaussian processesFrom1 Jan 2019 → 31 Dec 2022Funding: FWO research project (including WEAVE projects)
- Incorporating error control in sparse modellingFrom1 Jan 2016 → 31 Dec 2019Funding: FWO research project (including WEAVE projects)
- Development of machine learning techniques for flow cytometry dataFrom1 Jan 2014 → 31 Dec 2017Funding: IWT personal funding - specialisation scholarships
- Fast and Reliable Assessment of Electromagnetic Field Exposure using Sequential Sampling, Surrogate Modeling and OptimizationFrom1 Jan 2014 → 31 Dec 2019Funding: FWO research project (including WEAVE projects)
- High-dimensional and stochastic macromodeling of parameterized high-speed systemsFrom1 Oct 2013 → 30 Sep 2016Funding: BOF - Other initiatives, FWO fellowships
- Versatile surrogate-based optimization of medium-scale and large-scale problems.From1 Oct 2013 → 30 Sep 2019Funding: FWO fellowships, BOF - Other initiatives
- Accurate parameterized macromodels for efficient designFrom1 Jan 2013 → 31 Dec 2018Funding: FWO research project (including WEAVE projects)
Publications
31 - 40 of 430
- Sensor fusion using backward shortcut connections for sleep apnea detection in multi-modal data(2020)Volume: 116
Authors: Tom Van Steenkiste, Dirk Deschrijver, Tom Dhaene
Pages: 112 - 125 - A voltage and current measurement dataset for plug load appliance identification in households(2020)
Authors: Roberto Medico, Leen De Baets, Jingkun Gao, Suman Giri, Emre Kara, Tom Dhaene, Chris Develder, Mario Berges, Dirk Deschrijver
- Multi-objective Bayesian optimization for engineering simulation(2020)Series: Studies in Computational Intelligence
Authors: Joachim van der Herten, Nicolas Knudde, Ivo Couckuyt, Tom Dhaene, T Bartz-Beielstein, B Filipic, P Korosec, E Talbi
Pages: 47 - 68 - A set based design method using bayesian active learning(2020)
Authors: K. Shintani, T. Sugai, J. Ishizaki, Nicolas Knudde, Ivo Couckuyt, Tom Dhaene
Number of pages: 1 - A Bayesian optimisation procedure for estimating optimal trajectories in electromagnetic compliance testing(2020)Volume: 3
Authors: Rémi Delanghe, Tom Van Steenkiste, Ivo Couckuyt, Dirk Deschrijver, Tom Dhaene
Number of pages: 1 - Multi-objective optimization of a wing fence on an unmanned aerial vehicle using surrogate-derived gradients(2020)
Authors: Jolan Wauters, Ivo Couckuyt, Nicolas Knudde, Tom Dhaene, Joris Degroote
Pages: 353 - 364 - Bayesian active learning for electromagnetic structure design(2020)
Authors: Jixiang Qing, Nicolas Knudde, Ivo Couckuyt, Domenico Spina, Tom Dhaene
Number of pages: 1 - Data-efficient Gaussian process regression for accurate visible light positioning(2020)
Authors: Nicolas Knudde, Willem Raes, Jorik De Bruycker, Tom Dhaene, Nobby Stevens
Pages: 1705 - 1709 - Fast characterization of input-output behavior of non-charge-based logic devices by machine learning(2020)
Authors: Arun Kaintura, Kyle Powers Foss, Odysseas Zografos, Ivo Couckuyt, Adrien Vaysset, Tom Dhaene, Bart Soree
- A machine learning-based epistemic modeling framework for EMC and SI assessment(2020)
Authors: Duygu De Witte, Simon De Ridder, Domenico Spina, Tom Dhaene, Hendrik Rogier, Dries Vande Ginste, Flavia Grassi
Number of pages: 1