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Related Experiment Video

Updated: Mar 10, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Network inference in the nonequilibrium steady state.

Simon L Dettmer¹, H Chau Nguyen², Johannes Berg¹

¹Institute for Theoretical Physics, University of Cologne, Zülpicher Straße 77, 50937 Cologne, Germany.

Physical Review. E

|December 15, 2016

Summary

This summary is machine-generated.

Researchers developed methods to infer model parameters in nonequilibrium systems, which lack a steady-state characterization. This is crucial for applications like neural networks and gene regulatory networks.

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Area of Science:

Statistical physics
Complex systems modeling

Background:

Nonequilibrium systems lack a Boltzmann-like steady-state distribution.
This poses challenges for parameter inference in models without detailed balance.
Such systems are relevant to neural networks and gene regulatory networks.

Purpose of the Study:

To develop methods for inferring model parameters from nonequilibrium steady-state samples.
To address the challenge of parameter estimation in systems lacking detailed balance.

Main Methods:

Focus on the asymmetric Ising model as a paradigmatic example.
Developed an exact inference algorithm.
Created an approximate algorithm for weak interactions using mean-field expansion.

Main Results:

Demonstrated parameter inference from independent samples of the nonequilibrium steady state.
Derived expressions for magnetizations and spin correlations.
Established sufficiency of these observables for parameter inference.

Conclusions:

Parameter inference is achievable for nonequilibrium systems using steady-state samples.
The developed algorithms offer exact and approximate solutions.
Symmetries in mean-field expansion help distinguish dynamics.