arXiv:2606.12318v1 Announce Type: new Abstract: Neural operators approximate mappings between function spaces, but often generalize poorly to other operators and usually require fine-tuning or retraining. In-Context Operator Networks (ICON) addresses this issue by prompting the model with numerical context so that the model learns specific operators from prompts and adapt to different operators without fine-tuning. However, ICON may still fail to generalize to out-of-distribution (OOD) operator tasks. Inpired by the success of harness engineering of Large Language models (LLMs), we introduce C
The continuous improvement in AI models is driving research into more generalized and adaptable learning methods, particularly for complex functional mappings.
This research addresses a critical limitation of neural operators, their poor generalization to out-of-distribution tasks, which hinders their broader applicability in scientific and engineering fields.
The ability of AI models to learn and adapt to diverse operators with minimal fine-tuning will accelerate scientific discovery and engineering design by making operator learning more robust and flexible.
- · AI research institutions
- · Engineering sectors (e.g., fluid dynamics, material science)
- · Scientific computing
- · Companies reliant on highly specialized, fine-tuned AI models
- · Traditional simulation methods
Operators will become more generalizable and adaptable without extensive retraining.
This improved generalization will accelerate the development of AI-driven solutions for complex physical and engineering problems.
Advances in operator learning could enable fully autonomous design and optimization cycles in various industries, reducing human intervention significantly.
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Read at arXiv cs.LG