arXiv subjects

Marc Berneman

Publications and source records attributed to Marc Berneman.

4 recordsLinked to original sources

arXiv2026

Training overdamped dynamics

In regimes where inertia is negligible, the temporal evolution is governed by overdamped dynamics. This limit is particularly relevant in soft-matter contexts, such as polymers, colloidal suspensions, and processes occurring at the cellular scale. Being able to manipulate the dynamics of such many-particle systems would enable control over rate-dependent elastic responses, time-dependent material properties, relaxation processes, and perhaps the hydrodynamics of suspensions. In this work, we develop a framework for manipulating overdamped dynamics through local, physically motivated update rules. Our approach is inspired by ideas from physical learning and directed aging, in which microscopic parameters adapt autonomously to endow a material with a desired function. Using the Rayleighian formulation, whose minimization reproduces the overdamped equations of motion, we derive approximate directed-aging and equilibrium-propagation rules tailored to dissipative systems. To demonstrate these ideas, we study a disordered Maxwell material that behaves elastically at short times but flows at long times. By locally modifying the viscous damping, we show that one can tune the viscous Poisson's ratio and shape local mechanical responses. These results illustrate how materials can be trained to exhibit targeted rate-dependent elastic and viscous behaviors.

cond-mat.soft↗

arXiv2025

Equilibrium Propagation for Dissipative Dynamics

Computing gradients of a cost function is central to design-based optimization and machine learning algorithms. Equilibrium propagation provides an exact method to compute gradients in hardware by exploiting the inherent physical laws. The locality of these algorithms, in conjunction with local updates, enables mechanical and electronic systems that autonomously learn a function. We extend these methods to damped dynamical systems operating in the linear regime, such as mechanical structures obeying damped Newtonian dynamics and RLC circuits. By introducing an effective action whose extremum corresponds to the underlying dynamics, we derive local learning rules. This approach applies both to problems with periodic boundary conditions and to those with resting initial conditions. We demonstrate the viability of our method in mechanical and electronic systems and explore novel functionality such as classifying temporal sound signals. Our work opens the door to intelligent materials that process dynamical signals, enabling temporal computations, passive and active sensors, and materials that act as frequency-dependent filters.

cond-mat.dis-nn↗

arXiv2024

Designing precise dynamical steady states in disordered networks

Elastic structures can be designed to exhibit precise, complex, and exotic functions. While recent work has focused on the quasistatic limit governed by force balance, the mechanics at a finite driving rate are governed by Newton's equations. The goal of this work is to study the feasibility, constraints, and implications of creating disordered structures with exotic properties in the dynamic regime. The dynamical regime offers responses that cannot be realized in quasistatics, such as responses at an arbitrary phase, frequency-selective responses, and history-dependent responses. We employ backpropagation through time and gradient descent to design spatially specific steady states in disordered spring networks. We find that a broad range of steady states can be achieved with small alterations to the structure, operating both at small and large amplitudes. We study the effect of varying the damping, which interpolates between the underdamped and the overdamped regime, as well as the amplitude, frequency, and phase. We show that convergence depends on several competing effects, including chaos, large relaxation times, a gradient bias due to finite time simulations, and strong attenuation. By studying the eigenmodes of the linearized system, we show that the systems adapt very specifically to the task they were trained to perform. Our work demonstrates that within physical bounds, a broad array of exotic behaviors in the dynamic regime can be obtained, allowing for a richer range of possible applications.

cond-mat.soft↗

arXiv2021

Modeling and control of 5-DoF boom crane

Automation of cranes can have a direct impact on the productivity of construction projects. In this paper, we focus on the control of one of the most used cranes, the boom crane. Tower cranes and overhead cranes have been widely studied in the literature, whereas the control of boom cranes has been investigated only by a few works. Typically, these works make use of simple models making use of a large number of simplifying assumptions (e.g. fixed length cable, assuming certain dynamics are uncoupled, etc.) A first result of this paper is to present a fairly complete nonlinear dynamic model of a boom crane taking into account all coupling dynamics and where the only simplifying assumption is that the cable is considered as rigid. The boom crane involves pitching and rotational movements, which generate complicated centrifugal forces, and consequently, equations of motion highly nonlinear. On the basis of this model, a control law has been developed able to perform position control of the crane while actively damping the oscillations of the load. The effectiveness of the approach has been tested in simulation with realistic physical parameters and tested in the presence of wind disturbances.

eess.SY↗