Physical Review E

Aims & Scope

Physical Review E (PRE) is a broad and interdisciplinary journal focusing on collective phenomena of many-body systems.

As the premier journal in the interrelated areas of statistical, nonlinear, biological, and soft matter physics, PRE covers recent developments in complex fluids, polymers, liquid crystals, and granular materials.

The journal also includes sections on solid mechanics, fluid dynamics, plasma physics, computational physics, networks, and complex systems.

Established in 1993, PRE is distinguished by the breadth of the subject areas it covers and its wide distribution and readership.

PRE provides an authoritative venue for high-quality work in traditional and emerging research areas, making it an essential resource for multiple disciplines.

PRE coordinates with other members of the Physical Review journal family to serve new subspecialties as they develop.

PRE publishes detailed research articles as well as Rapid Communications, which are short papers of particular significance and/or topical interest.

The journal has a flexible approach to article lengths and welcomes submission of longer papers that provide depth and authority in their subject areas.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.4
2024	2.20

SJR (SCImago Journal Rank)

Year	Value
2024	0.705

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	271

Journal Rank

Year	Value
2024	8041

Journal Citation Indicator

Year	Value
2024	12241

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Mathematics and Physics and Astronomy, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Reconstruction of three-dimensional porous media using generative adversarial neural networks

  Citation: 399

  Authors: Lukas, Olivier, Martin J.

• Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders

  Citation: 370

  Authors: Sebastian J.

• Versatile and efficient pore network extraction method using marker-based watershed segmentation

  Citation: 301

  Authors: Jeff T.

• Quantum chaos challenges many-body localization

  Citation: 282

  Authors: Jan, Janez, Tomaž, Lev

• Generalized network modeling: Network extraction as a coarse-scale discretization of the void space of porous media

  Citation: 278

  Authors: Ali Q., Branko, Martin J.

• Ecological communities with Lotka-Volterra dynamics

  Citation: 271

  Authors: Guy

• Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination

  Citation: 266

  Authors: Wenjian, Rajiv R. P., Richard T.

• Equivalence between modularity optimization and maximum likelihood methods for community detection

  Citation: 218

  Authors: M. E. J.

• Proof of the finite-time thermodynamic uncertainty relation for steady-state currents

  Citation: 217

  Authors: Jordan M., Todd R.

• Universal bounds on current fluctuations

  Citation: 213

  Authors: Patrick, Andre C., Udo

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