arXiv:2605.26718v1 Announce Type: new Abstract: Efficient onboard multi-field sparse reconstruction is essential for the autonomous operation of aerospace vehicles. While existing deep learning models exhibit promise for single-field reconstruction, deploying multiple independent models leads to prohibitive model size growth and fails to exploit cross-field correlations, particularly under few-shot conditions. To address these challenges, we first propose a lightweight multi-task Fourier neural operator (MTL-FNO), an end-to-end joint training framework based on hard parameter sharing. In each
The increasing complexity of autonomous systems, particularly in aerospace, is driving demand for efficient, lightweight AI models that can operate on resource-constrained onboard hardware.
This development proposes a method to significantly reduce the computational footprint and improve the efficiency of AI models for multi-field data reconstruction, critical for autonomous vehicle performance and broader edge AI applications.
Traditional approaches using multiple independent models for multi-field data will be challenged by consolidated, lightweight frameworks that better exploit cross-field correlations, leading to more efficient and capable autonomous systems.
- · Aerospace companies
- · Autonomous vehicle developers
- · Edge AI hardware manufacturers
- · Deep learning researchers in efficient architectures
- · Developers relying on heavyweight, single-task AI models for edge applications
- · Companies unable to integrate multi-task learning frameworks
Improved performance and reduced power consumption for AI systems embedded in aerospace vehicles and other autonomous platforms.
Accelerated development and broader adoption of AI in computationally limited environments due to increased model efficiency.
Potential for new AI applications requiring real-time multi-sensor fusion and reconstruction on edge devices, beyond current capabilities.
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Read at arXiv cs.LG