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 through explicit discrepancy modeling. However, its non-amortized, per-instance formulation limits scalability across families of related systems. We introduce Amortized Physics-Informed Calibration (APIC), a population-level extension of KOH that leverages Neural Processes to perform scalable Bayesian inference acros
The increasing sophistication and scale of AI models necessitate more robust and scalable calibration methods to bridge the gap between theoretical physics models and real-world data, making this research timely.
This development offers a significant step towards more accurate and scalable real-world applications of physics-informed AI, crucial for critical infrastructure, scientific discovery, and autonomous systems.
The ability to perform amortized, population-level calibration of physics-informed models means AI systems can now incorporate real-world discrepancies more efficiently and broadly, improving reliability and applicability.
- · AI developers
- · Engineering sectors
- · Scientific research
- · Autonomous systems manufacturers
- · Traditional calibration methods
- · Systems heavily reliant on perfectly specified physics models
Improved reliability and predictive power of AI models in complex physical systems.
Accelerated development and deployment of AI in fields like climate modeling, material science, and personalized medicine.
Enhanced trust in AI-driven decision-making for high-stakes applications due to better calibrated predictions.
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Read at arXiv cs.LG