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Dynamic Mode Decomposition applied to noisy thermal system data

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A new research paper explores the application of Dynamic Mode Decomposition (DMD) for analyzing thermal systems with noisy and low-resolution data. The study examines how preprocessing and truncation strategies can improve the stability and interpretability of DMD in these challenging conditions. Results show that DMD can effectively capture dominant thermal behaviors from sparse and degraded datasets when the number of retained modes is carefully selected, balancing reconstruction fidelity with noise sensitivity. AI

RANK_REASON Research paper published on arXiv detailing a computational physics method. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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Dynamic Mode Decomposition applied to noisy thermal system data

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Research paper published on arXiv detailing a computational physics method. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · M. E. P. Silva, L. S. Araujo, F. T. Colombo, A. Cunha Jr, S. da Silva ·

    Characterization of Thermal Systems from Noisy and Low-resolution Measurements Using Dynamic Mode Decomposition

    arXiv:2608.14581v1 Announce Type: cross Abstract: Thermal monitoring in practical applications is often constrained by sparse sensing, measurement noise, and limited spatial resolution, which hinder the identification of heat transfer dynamics. In such settings, calibrating high-…

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