184 citations · 318 across the 26 of their papers we have counts for
29 papers
MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimization
Lucas Palazzolo, Mickaël Binois, Laëtitia Giraldi
Many real-world optimization problems rely on expensive simulations or experiments, making the efficient use of available data essential. Multi-fidelity optimization of high-dimens…
Trajectory-Oriented Optimization Via Adaptive Thompson Sampling And Grid Refinement: A Tutorial With The ADAPTIVE\_TS Package
David O'Gara, Arindam Fadikar, Mickaël Binois +2
Stochastic simulators are increasingly used to expand the frontier of scientific knowledge and inform decision-making across real-world contexts. Simulator calibration, a process b…
Optimal Control of Microswimmers for Trajectory Tracking Using Bayesian Optimization
Lucas Palazzolo, Mickaël Binois, Laëtitia Giraldi
Trajectory tracking for microswimmers remains a key challenge in microrobotics, where low-Reynolds-number dynamics make control design particularly complex. In this work, we formul…
Non-Locally Controllable but Trackable Magnetic Head Flagellated Swimmer
Lucas Palazzolo, Mickaël Binois, Laëtitia Giraldi
Unlike macroscopic swimmers, microswimmers operate in a low-Reynolds-number regime dominated by viscous forces. This paper investigates the controllability of a magnetic microswimm…
Staying on Track: Efficient Trajectory Discovery with Adaptive Batch Sampling
Arindam Fadikar, Abby Stevens, Mickael Binois +3
Bayesian optimization (BO) is a powerful framework for estimating parameters of expensive simulation models, particularly in settings where the likelihood is intractable and evalua…
Modular Jump Gaussian Processes
Anna R. Flowers, Christopher T. Franck, Mickaël Binois +2
Gaussian processes (GPs) furnish accurate nonlinear predictions with well-calibrated uncertainty. However, the typical GP setup has a built-in stationarity assumption, making it il…