eess.SY · 2026-05-22 · No. 7

Systems and Control, 2026-05-22.

1 new papers in eess.SY. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

1 entries
  1. 01

    Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review

    Joseph Nyangon

    eess.SY · cs.AI · cs.LG · cs.NE

    The integration of machine learning with domain-specific physics is transforming the design, monitoring, and control of electricity systems, where data scarcity, limited interpretability, and the need to enforce physical laws constrain purely data-driven models. Physics-informed machine learning (PIML) addresses these limitations by embedding governing equations directly into the learning process, yielding accurate, efficient, and scalable...

    arxiv.org/abs/2605.21903 · PDF

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