arXiv:2606.07153v1 Announce Type: cross Abstract: Physics-informed learning is increasingly used for partial differential equation (PDE)-governed inverse problems, but its reliability remains difficult to certify. This paper develops a no-harm certification-and-selection framework for physics-informed inverse learning. A learned reconstruction is accepted only when its residual-calibrated radius is no worse than the baseline radius, namely when $$R_{\mathrm{learn}}\le R_{\mathrm{base}}+\varepsilon_{\mathrm{safe}};$$otherwise, the method returns the baseline. The certificate combines data, phys
The increasing adoption of physics-informed AI models in critical applications necessitates robust methods for certifying their reliability and managing uncertainty, which this paper addresses.
This development improves the trustworthiness of AI models in scientific and engineering inverse problems, potentially accelerating their deployment in high-stakes fields where accuracy and certifiability are paramount.
The introduction of a 'no-harm' certification framework for physics-informed learning establishes a new standard for model acceptance, ensuring that learned reconstructions are at least as reliable as baseline methods.
- · AI/ML researchers
- · Engineering firms
- · Scientific research institutions
- · AI models without robust uncertainty quantification
- · Fields heavily relying on unreliable inverse problem solutions
Increased confidence in AI-driven solutions for complex scientific and engineering problems.
Faster adoption of AI in fields requiring high-reliability outputs, such as materials science or climate modeling.
Reduced experimental costs and accelerated discovery cycles in scientific and industrial R&D due to more trustworthy AI simulations.
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Read at arXiv cs.LG