Zobrazeno 1 - 10
of 1 062
pro vyhledávání: '"DILL, KEN A."'
Autor:
Yang, Ying-Jen, Dill, Ken A.
In this and a companion paper [arXiv:2410.09277], we give a general and comprehensive theory for nonequilibrium (NEQ) network forces and flows (Caliber Force Theory, CFT). It follows the "Two Laws" structure of Equilibrium Thermodynamics, where a Fir
Externí odkaz:
http://arxiv.org/abs/2410.17495
Autor:
Yang, Ying-Jen, Dill, Ken A.
Non-EQuilibrium (NEQ) statistical physics has not had the same depth of rigor and generality of foundational grounding as that of EQuilibrium (EQ) statistical physics, where forces and fluctuational response functions are derived from potentials such
Externí odkaz:
http://arxiv.org/abs/2410.09277
Autor:
Kocher, Charles D., Dill, Ken A.
The origin of life must have been preceded by Darwin-like evolutionary dynamics that could propagate it. How did that adaptive dynamics arise? And from what prebiotic molecules? Using evolutionary invasion analysis, we develop a universal framework f
Externí odkaz:
http://arxiv.org/abs/2311.13650
Statistical physics aims to describe properties of macroscale systems in terms of distributions of their microscale agents. Its central tool is the maximization of entropy, a variational principle. We review the history of this principle, first consi
Externí odkaz:
http://arxiv.org/abs/2310.06070
Autor:
Pachter, Jonathan Asher, Dill, Ken A.
Important models of nonequilibrium statistical physics (NESP) are limited by a commonly used, but often unrecognized, near-equilibrium approximation. Fokker-Planck and Langevin equations, the Einstein and random-flight diffusion models, and the Schna
Externí odkaz:
http://arxiv.org/abs/2204.13204
Autor:
Glukhov, Ernest, Kalitin, Dmytro, Stepanenko, Darya, Zhu, Yimin, Nguyen, Thu, Jones, George, Patsahan, Taras, Simmerling, Carlos, Mitchell, Julie C., Vajda, Sandor, Dill, Ken A., Padhorny, Dzmitry, Kozakov, Dima
Publikováno v:
In Biophysical Journal 3 September 2024 123(17):2902-2909
The relationship between complex, brain oscillations and the dynamics of individual neurons is poorly understood. Here we utilize Maximum Caliber, a dynamical inference principle, to build a minimal, yet general model of the collective (mean-field) d
Externí odkaz:
http://arxiv.org/abs/2008.04940
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 measure signals from individual neurons in the brain, and infer how they
Externí odkaz:
http://arxiv.org/abs/2004.02318
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.