arXiv:2606.00716v1 Announce Type: new Abstract: Inference and control in engineered physical systems pay a heavy physics cost at deployment: state estimators, inverse-problem solvers, model-predictive controllers, schedulers, and observers are often not closed-form and must re-solve a numerical optimization per instance, with the operator re-supplied each time. Physics-informed learning moves this cost to training, but uses a single encoder pathway whose latent geometry de-learns under fine-tuning and admits no quantitative transfer guarantee. We propose an asymmetric two-pathway architecture
The paper addresses a critical limitation in current physics-informed learning, where model adaptability and transferability remain significant challenges, hindering broader deployment in complex physical systems.
This research proposes a method to improve the robustness and generalizability of AI models in engineered systems, potentially reducing operational costs and accelerating the development of autonomous capabilities.
The proposed asymmetric two-pathway architecture aims to provide quantitative transfer guarantees and prevent 'de-learning' during fine-tuning, making AI models more reliable and adaptable for real-world applications.
- · AI/ML researchers
- · Robotics and automation industry
- · Aerospace and defence
- · Industrial control systems developers
- · Developers of custom, single-purpose AI models
- · Companies relying on expensive, continuous re-optimization
More efficient and adaptable AI models for state estimation and control in physical systems become feasible.
Reduced development and deployment costs for autonomous systems lead to faster adoption across various industries.
Enhanced AI capabilities could unlock new complex engineering challenges that were previously intractable due to AI's limitations in robustness and transferability.
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Read at arXiv cs.LG