physics.flu-dyn · 2026-07-27 · No. 66

Fluid Dynamics, 2026-07-27.

2 new papers in physics.flu-dyn. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

2 entries
  1. 01

    PRIMS: Physics-guided Representation for Fluid Identification in Multimodal Sensing

    Hai-Long Nguyen, Trung Thanh Nguyen, Lars Holm, Dennis Alveringh, Duc Viet Le

    physics.flu-dyn · cs.AI

    Accurate on-device fluid identification is essential for microfluidic applications, yet maintaining reliability under varying flow, pressure, and temperature remains a key challenge. Existing learning-based methods often treat sensor signals as domain-agnostic features, neglecting the underlying physical relationships that govern fluid behavior, thereby limiting generalization and interpretability. To address this, we propose PRIMS, a...

    arxiv.org/abs/2607.22422 · PDF

  2. 02

    Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows

    Harish Ramachandran, Björn Kimpel, Thomas Paula, Josef Winter, Steffen Schmidt, Nikolaus Adams

    physics.flu-dyn · cs.AI

    Compressible multiphase flows involving shocks and material interfaces arise in applications such as bubble collapse and droplet breakup, where strong nonlinear interactions produce complex interface deformation, mixing, and multiscale dynamics. Developing reliable machine learning surrogates for these flows remains challenging due to the simultaneous presence of compressibility, sharp discontinuities, and multiphase effects. In this work, we...

    arxiv.org/abs/2607.22280 · PDF

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