arXiv:2605.26732v1 Announce Type: new Abstract: Learning-based surrogates have become increasingly effective for wave-field prediction, and neural operators in particular have shown strong performance within observed frequency regimes. However, higher-frequency prediction under scarce target supervision remains comparatively underexplored, especially in wave problems where higher-frequency data are substantially more expensive to simulate or measure than lower-frequency data. A central difficulty is that cross-frequency transfer is inherently asymmetric: coarse amplitude structure remains rela
This research addresses a critical gap in machine learning for scientific prediction, specifically in higher-frequency wave phenomena where data acquisition is costly, making advancements in data-scarce learning highly relevant.
Improving wave-field prediction for higher frequencies with limited data has implications across various scientific and engineering applications, from materials science to defense, enabling more efficient and accurate simulations and predictions.
This advancement potentially shifts the paradigm for high-fidelity physical simulations, making complex wave phenomena more accessible to prediction via AI, even when extensive training data is unavailable.
- · Scientific research institutions
- · Engineering firms
- · Defense contractors
- · ML model developers
- · Traditional high-cost simulation methods
- · Industries reliant on extensive physical testing
More accurate and faster predictive models for complex physical systems will emerge, particularly in areas like radar, sonar, and geological surveys.
Reduced R&D cycles and costs for new materials and system designs that operate at higher frequencies due to improved predictive capabilities.
Enhanced AI-driven autonomous systems that rely on high-frequency wave interactions, leading to advancements in sensor technology and environmental understanding.
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Read at arXiv cs.LG