arXiv:2603.22050v2 Announce Type: replace-cross Abstract: Supervised machine learning describes the practice of fitting a parameterized model to labeled input-output data. Supervised machine learning methods have demonstrated promise in learning efficient surrogate models that can (partially) replace expensive high-fidelity models, making many-query analyses, such as optimization, uncertainty quantification, and inference, tractable. However, when training data must be obtained through the evaluation of an expensive model or experiment, the amount of training data that can be obtained is often
The continuous drive for more efficient and robust machine learning models, especially in data-scarce environments, necessitates ongoing research into advanced techniques like multifidelity-augmented Gaussian processes.
This research is crucial for addressing the high cost and scarcity of data in many real-world applications, which currently limits the widespread adoption of AI in critical sectors.
The development of more efficient surrogate models could reduce the computational and experimental burden of AI training, making advanced simulations and analyses more accessible.
- · Sectors with expensive data (e.g., aerospace, drug discovery)
- · AI/ML researchers
- · High-fidelity model developers
- · Traditional, data-intensive machine learning approaches using only high-fidelity
More accurate and faster surrogate models for complex systems lead to accelerated research and development cycles.
Reduced need for vast datasets could democratize access to advanced AI capabilities for organizations with limited resources.
New applications of AI become feasible in fields where data acquisition was previously a prohibitive bottleneck.
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Read at arXiv cs.LG