Search Results for author:
Found 4 papers, 2 papers with code
Inferring the Langevin Equation with Uncertainty via Bayesian Neural Networks
Pervasive across diverse domains, stochastic systems exhibit fluctuations in processes ranging from molecular dynamics to climate phenomena.
Attaining entropy production and dissipation maps from Brownian movies via neural networks
We show that our method accurately measures the EP and creates a dissipation map in two nonequilibrium systems, the bead-spring model and a network of elastic filaments.
Inertial effects on the Brownian gyrator
Another unique feature of the Langevin description is that rotation is maximized at a particular anisotropy while the stability of the rotation is minimized at a particular anisotropy or mass.
Statistical Mechanics
Learning entropy production via neural networks
This Letter presents a neural estimator for entropy production, or NEEP, that estimates entropy production (EP) from trajectories of relevant variables without detailed information on the system dynamics.
