Bayesian Active Learning for EMI Near-Field Emission Characterization of High-Speed Electronics

Accurate parameterized macromodels for efficient design

Advanced 3D MOR-subgridding

Advancing Engineering Design and Optimization through Data-efficient Machine Learning

Development of machine learning techniques for flow cytometry data

Efficient ensemble systems for classifying large-scale data

Fast and Reliable Assessment of Electromagnetic Field Exposure using Sequential Sampling, Surrogate Modeling and Optimization

Generative design of linear passive electronic systems

High bitrate channel modeling and transceiver optimization for advanced chip-to-chip interconnects and VDSL applications

High-dimensional and stochastic macromodeling of parameterized high-speed systems

Incorporating error control in sparse modelling

Learning complex patterns with Gaussian processes

Machine Learning and Radar Sensors for Monitoring Patients and Elderly People in Healthcare

Machine learning for meta-modelling in distributed computing environments

Near-field measurement techniques for the efficient solving EMC emission Problem

Parameterized model order reduction of delayed and distrubuted systems (D-PMOR)

Parametrized Model Order Reduction (PMOR): Sparse Data and Sparse Models

Robuste techniques for multivariate surrogate modeling

Versatile surrogate-based optimization of medium-scale and large-scale problems.

Wideband parameterized behavioral models for passive and active electronic components,