6 papers
Discovering and decoding latent mean-field structure with variational autoencoders
Marco Biroli, Max Welling, Vincenzo Vitelli
Generative models are increasingly used to capture correlations in many-body systems, but the representations they learn remain largely opaque to physical interpretation. Here, we…
Dynamically emergent correlations in Brownian particles subject to simultaneous non-Poissonian resetting protocols
Gabriele de Mauro, Marco Biroli, Satya N. Majumdar +1
We consider a one-dimensional gas of independent Brownian particles subject to simultaneous stochastic resetting, with inter-reset times drawn from a general waiting-time distr…
First Passage Resetting Gas
Marco Biroli, Satya N. Majumdar, Gregory Schehr
We study a one-dimensional gas of Brownian particles that diffuse independently but are simultaneously reset whenever any of them reaches a fixed threshold located at .…
Strongly correlated stochastic systems
Marco Biroli
This thesis develops exact analytical tools to study strongly correlated stochastic systems, with a focus on extreme value statistics, gap statistics, and full counting statistics…
Experimental evidence for strong emergent correlations between particles in a switching trap
Marco Biroli, Sergio Ciliberto, Manas Kulkarni +3
We experimentally study a system of two-dimensional Brownian particles, each confined in a harmonic trap with identical stiffness. The stiffness switches simultaneously bet…
Resetting Dyson Brownian motion
Marco Biroli, Satya N. Majumdar, Gregory Schehr
In this paper, we introduce a new stochastic process of interacting particles on the line that evolve via Dyson Brownian motion (DBM) with Dyson's index and undergo sim…