143 citations · 740 across the 26 of their papers we have counts for
26 papers
Non-Equilibrium Statistical Physics Beyond the Ideal Heat Bath Approximation
Jonathan Asher Pachter, Ken A. Dill
Important models of nonequilibrium statistical physics (NESP) are limited by a commonly used, but often unrecognized, near-equilibrium approximation. Fokker-Planck and Langevin equ…
The refractory period matters: unifying mechanisms of macroscopic brain waves
Corey Weistuch, Lilianne R. Mujica-Parodi, Ken Dill
The relationship between complex, brain oscillations and the dynamics of individual neurons is poorly understood. Here we utilize Maximum Caliber, a dynamical inference principle,…
Inferring a network from dynamical signals at its nodes
Corey Weistuch, Luca Agozzino, Lilianne R. Mujica-Parodi +1
We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we mea…
Minimal constraints for Maximum Caliber analysis of dissipative steady state systems
Luca Agozzino, Ken A Dill
Maximum Caliber (Max Cal) is purported to be a general variational principle for Non-Equilibrium Statistical Physics (NESP). But recently, Jack and Evans and Maes have raised conce…
Maximum Caliber: a general variational principle for dynamical systems
Purushottam D. Dixit, Jason Wagoner, Corey Weistuch +3
We review here {\it Maximum Caliber} (Max Cal), a general variational principle for inferring distributions of paths in dynamical processes and networks. Max Cal is to dynamical tr…
Caliber Corrected Markov Modeling (C2M2): Correcting Equilibrium Markov models
Purushottam D. Dixit, Ken A. Dill
Rate processes are often modeled using Markov-State Models (MSM). Suppose you know a prior MSM, and then learn that your prediction of some particular observable rate is wrong. Wha…