arXiv subjects

Laëtitia Giraldi

Publications and source records attributed to Laëtitia Giraldi.

8 recordsLinked to original sources

arXiv2026

MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimization

Many real-world optimization problems rely on expensive simulations or experiments, making the efficient use of available data essential. Multi-fidelity optimization of high-dimensional black-box functions subject to black-box constraints is increasingly relevant as the cost of objective evaluations continues to rise in applications such as machine learning, engineering, and control. To our knowledge, no existing method simultaneously addresses high-dimensionality, black-box constraints, an arbitrary number of fidelity levels, and non-nested sampling. In this work, we extend the Scalable Constrained Bayesian Optimization method to the multi-fidelity setting, resulting in the MF-SCBO method. The proposed approach is evaluated on standard benchmark functions as well as challenging problems. The experimental results demonstrate that MF-SCBO generally achieves better convergence than both the single-fidelity SCBO and the other multi-fidelity method considered in this high-dimensional and constrained settings.

cs.LG↗

arXiv2026

Optimal Control of Microswimmers for Trajectory Tracking Using Bayesian Optimization

Trajectory tracking for microswimmers remains a key challenge in microrobotics, where low-Reynolds-number dynamics make control design particularly complex. In this work, we formulate the trajectory tracking problem as an optimal control problem and solve it using a combination of B-spline parametrization with Bayesian optimization, allowing the treatment of high computational costs without requiring complex gradient computations. Applied to a flagellated magnetic swimmer, the proposed method reproduces a variety of target trajectories, including biologically inspired paths observed in experimental studies. We further evaluate the approach on a three-sphere swimmer model, demonstrating that it can adapt to and partially compensate for wall-induced hydrodynamic effects. The proposed optimization strategy can be applied consistently across models of different fidelity, from low-dimensional ODE-based models to high-fidelity PDE-based simulations, showing its robustness and generality. These results highlight the potential of Bayesian optimization as a versatile tool for optimal control strategies in microscale locomotion under complex fluid-structure interactions.

cs.RO↗

arXiv2025

Harnessing swarms for directed migration of interacting active particles via optimal global control

This study investigates the use of global control strategies to enhance the directed migration of swarms of interacting self-propelled particles confined in a channel. Uncontrolled dynamics naturally leads to wall accumulation, clogging, and band formation due to the interplay between self-organization and confinement. This work explores whether a uniform global control, such as magnetic field acting on all particles, can optimize collective transport. Using a discrete Vicsek-like model, it is found that simple global alignment controls, optimized via reinforcement learning, efficiently suppress unfavorable configurations and significantly increase the net particle flux along a prescribed channel direction. These results highlight that coarse, system-level observations are sufficient to achieve near-optimal control, even in regimes with strong fluctuations or partial ordering.

cond-mat.soft↗

arXiv2025

Two-way Coupling of Fluid--Structure Interaction for Elastic Magneto-Swimmers:A Finite Element ALE Approach

Artificial micro-swimmers actuated by external magnetic fields hold significant promise for targeted biomedical applications, including drug delivery and micro-robot-assisted therapy. However, their dynamics remain challenging to control due to the complex nonlinear coupling between magnetic actuation, elastic deformations, and fluid interactions in confined biological environments. Numerical modeling is therefore essential to better understand, predict, and optimize their behavior for practical applications. In this work, we present a comprehensive finite element framework based on the Arbitrary Lagrangian--Eulerian formulation to simulate deformable elastic micro-swimmers in confined fluid domains. The method employs a full-order model that resolves the complete fluid dynamics while simultaneously tracking swimmer deformation and global displacement on conforming meshes. Numerical experiments are performed with the open-source finite element library Feel++, demonstrating excellent agreement with experimental data from the literature. The validation benchmarks in both two and three dimensions confirm the accuracy, robustness, and computational efficiency of the proposed framework, representing a foundational step toward developing digital twins of magneto-swimmers for biomedical applications.

math.AP↗

arXiv2025

Non-Locally Controllable but Trackable Magnetic Head Flagellated Swimmer

Unlike macroscopic swimmers, microswimmers operate in a low-Reynolds-number regime dominated by viscous forces. This paper investigates the controllability of a magnetic microswimmer composed of a spherical magnetic head and an elastic, non-magnetic flagellum. The swimmer evolves in a Stokes flow and is modeled using the resistive force theory. We prove that, under planar motion, the system is not small-time locally controllable and numerically identify regions that remain inaccessible. Nevertheless, simulations show that trajectory tracking can still be achieved via Bayesian optimization, though it requires large-amplitude transverse deformations.

math.OC↗

arXiv2025

Parametric Shape Optimization of Flagellated Micro-Swimmers Using Bayesian Techniques

Understanding and optimizing the design of helical micro-swimmers is crucial for advancing their application in various fields. This study presents an innovative approach combining Free-Form Deformation with Bayesian Optimization to enhance the shape of these swimmers. Our method facilitates the computation of generic swimmer shapes that achieve optimal average speed and efficiency. Applied to both monoflagellated and biflagellated swimmers, our optimization framework has led to the identification of new optimal shapes. These shapes are compared with biological counterparts, highlighting a diverse range of swimmers, including both pushers and pullers.

math.OC↗

arXiv2024

Towards a computational framework using finite element methods with Arbitrary Lagrangian-Eulerian approach for swimmers with contact

Swimming involves a body's capability to navigate through a fluid by undergoing self-deformations. Typically, fluid dynamics are described by the Navier-Stokes equations, and when integrated with a swimming body, it results in a highly intricate model. This paper introduces a computational framework for simulating the movement of multiple swimmers with various geometries immersed in a Navier-Stokes fluid. The approach relies on the finite element method with an Arbitrary Lagrangian-Eulerian (ALE) framework to handle swimmer displacements. Numerous numerical experiments demonstrate the adaptability of the computational framework across various scenarios.All the implementations are made using the Feel++ finite element library.

math.AP↗

arXiv2024

Mathematical and computational framework for moving and colliding rigid bodies in a Newtonian fluid

We studied numerically the dynamics of colliding rigid bodies in a Newtonian fluid. The finite element method is used to solve the fluid-body interaction and the fluid motion is described in the Arbitrary-Lagrangian-Eulerian framework. To model the interactions between bodies, we consider a repulsive collision-avoidance model, defined by R. Glowinski. The main emphasis in this work is the generalization of this collision model to multiple rigid bodies of arbitrary shape. Our model first uses a narrow-band fast marching method to detect the set of colliding bodies. Then, collision forces and torques are computed for these bodies via a general expression, which does not depend on their shape. Numerical experiments examining the performance of the narrow-band fast marching method and the parallel execution of the collision algorithm are discussed. We validate our model with literature results and show various applications of colliding bodies in two and three dimensions. In these applications, the bodies move due to forces such as gravity, a fluid flow, or their own actuation. Finally, we present a tool to create arbitrarily shaped bodies in discretized fluid domains, enabling conforming body-fluid interface and allowing to perform simulations of fluid-body interactions with collision treatment in these realistic environments. All simulations are conducted with the Feel++ open source library.

math.AP↗