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New method enhances equation discovery from noisy data using Koopman dynamics

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Researchers have developed a dynamics-aware method for identifying governing equations from sparse and noisy data, building upon techniques like Sparse Identification of Nonlinear Dynamics (SINDy) and PDE Functional Identification (PDE-FIND). The new approach utilizes Koopman-based upsampling methods, including Dynamic Mode Decomposition (DMD) and its variants, to interpolate and denoise data before derivative estimation and sparse regression. This preprocessing step aims to improve the accuracy of derivative estimation, which is often unreliable with sparse and noisy measurements. The method was tested on several ODE and PDE systems, showing that Koopman-based upsampling, particularly Polynomial EDMD for ODEs, offers performance gains over traditional interpolation techniques. AI

IMPACT Enhances the ability to discover underlying physical laws from experimental data, potentially accelerating scientific research.

RANK_REASON The cluster contains a research paper detailing a new methodology for scientific discovery. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method enhances equation discovery from noisy data using Koopman dynamics

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The cluster contains a research paper detailing a new methodology for scientific discovery. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pongpisit Thanasutives, Yoshinobu Kawahara ·

    Dynamics-aware identification of governing equations from sparse and noisy data

    arXiv:2607.29036v1 Announce Type: new Abstract: Sparse identification of nonlinear dynamics (SINDy) and PDE functional identification (PDE-FIND) recover parsimonious ordinary and partial differential equations (ODEs and PDEs) from data. However, sparse and noisy temporal measurem…

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