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New APIC method calibrates physics models using Neural Processes

By PulseAugur Editorial · [1 sources] ·

Researchers have developed APIC, a new method for calibrating physics models that suffer from discrepancies with real-world data. This approach extends the Kennedy-O'Hagan framework by using Neural Processes to enable scalable, population-level Bayesian inference. APIC's architecture separates instance-specific physical parameters from shared discrepancy structures, allowing for rapid calibration and uncertainty quantification of new, unseen systems. AI

IMPACT Introduces a novel amortized inference technique for improving physics model accuracy and uncertainty quantification.

RANK_REASON The cluster contains an academic paper detailing a new method for physics model calibration. [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 APIC method calibrates physics models using Neural Processes

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The cluster contains an academic paper detailing a new method for physics model calibration. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Aishwarya Venkataramanan, Sai Karthikeya Vemuri, Joachim Denzler ·

    APIC: Amortized Physics-Informed Calibration using Neural Processes

    arXiv:2606.03355v1 Announce Type: new Abstract: Physics models are inherently imperfect due to misspecified or missing mechanisms, resulting in systematic discrepancies between model predictions and real-world observations. The Kennedy-O'Hagan (KOH) framework addresses this issue…

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