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New framework decouples physics from discrepancy learning for system identification

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Orthogonal Discrepancy Kernels (ODKs), a novel semi-parametric framework designed for nonlinear system identification. This approach effectively separates discrepancy functions from physics-based components, enabling more interpretable models even with incomplete physical data. The framework utilizes orthogonal Gaussian process regression to balance sparse parameter selection with discrepancy learning. AI

RANK_REASON The cluster contains a research paper published on arXiv detailing a new framework for system identification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

New framework decouples physics from discrepancy learning for system identification

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The cluster contains a research paper published on arXiv detailing a new framework for system identification. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Swapnil Manna, Timothy J. Rogers, Lawrence Bull ·

    Orthogonal Discrepancy Kernels for Learning with Partial Physics

    arXiv:2606.21199v2 Announce Type: replace Abstract: We introduce a semi-parametric framework for nonlinear system identification, which decouples discrepancy functions from physics-based components. Orthogonal Gaussian process regression balances sparse parameter selection (the w…

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