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Fault-tolerant shallow recurrent decoders for sparse sensing with missing sensor measurements
This paper introduces fault-tolerant sparse sensing strategies for shallow recurrent decoders (SHRED) to handle missing, asynchronous, and heterogeneous sensor data. The authors propose transformer-based masking mechanisms and a multi-step decoding training objective, demonstrating improved reconstruction fidelity and robustness across synthetic and real-world hydrodynamic/ocean modeling benchmarks without added inference overhead.
Why it matters: Provides concrete, recent advances in robust AI-based state estimation for real-world sensing, with clear practical value for operational environments.
SHREDsensor fault tolerancetime-series reconstruction