• Open Access

Inferring dissipation maps from videos using convolutional neural networks

Youngkyoung Bae, Dong-Kyum Kim, and Hawoong Jeong
Phys. Rev. Research 4, 033094 – Published 1 August 2022
PDFHTMLExport Citation

Abstract

In the study of living organisms at mesoscopic scales, attaining a measure of dissipation or entropy production (EP) is essential to gain an understanding of their nonequilibrium dynamics. However, when tracking the relevant variables is impractical, it is challenging to figure out where and to what extent dissipation occurs from recorded time-series images from experiments. In this paper we develop an estimator that can, without detailed knowledge of the given systems, quantify the stochastic EP and produce a spatiotemporal pattern of the EP (or dissipation map) from videos through an unsupervised learning algorithm. Applying a convolutional neural network (CNN), our estimator allows us to visualize where the dissipation occurs as well as its time evolution in a video by looking at an attention map of the CNN's last layer. We demonstrate that our estimator accurately measures the stochastic EP and provides a locally heterogeneous dissipation map, which is mainly concentrated in the origins of a nonequilibrium state, from generated Brownian videos of various models. We further confirm high performance even with noisy, low-spatial-resolution data and partially observed situations. Our method will provide a practical way to obtain dissipation maps and ultimately contribute to uncovering the source and the dissipation mechanisms of complex nonequilibrium phenomena.

  • Figure
  • Figure
  • Figure
  • Figure
  • Figure
  • Received 28 January 2022
  • Accepted 15 July 2022

DOI:https://doi.org/10.1103/PhysRevResearch.4.033094

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Statistical Physics & Thermodynamics

Authors & Affiliations

Youngkyoung Bae1, Dong-Kyum Kim1,*, and Hawoong Jeong1,2,†

  • 1Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
  • 2Center for Complex Systems, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea

  • *Present address: Data Science Group, Institute for Basic Science, Daejeon 34126, Korea.
  • hjeong@kaist.edu

Article Text

Click to Expand

Supplemental Material

Click to Expand

References

Click to Expand
Issue

Vol. 4, Iss. 3 — August - October 2022

Subject Areas
Reuse & Permissions
Author publication services for translation and copyediting assistance advertisement

Authorization Required


×
×

Images

×

Sign up to receive regular email alerts from Physical Review Research

Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

×

Log In

Cancel
×

Search


Article Lookup

Paste a citation or DOI

Enter a citation
×