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We consider a discrete-time system where a resource-constrained source (e.g., a small sensor) transmits its time-sensitive data to a destination over a time-varying wireless channel. Each transmission incurs a fixed transmission cost (e.g., energy cost), and no transmission results in a staleness cost represented by the Age-of-Information. The source must balance the tradeoff between transmission and staleness costs. To address this challenge, we develop a robust online algorithm to minimize the sum of transmission and staleness costs, ensuring a worst-case performance guarantee. While online algorithms are robust, they are usually overly conservative and may have a poor average performance in typical scenarios. In contrast, by leveraging historical data and prediction models, machine learning (ML) algorithms perform well in average cases. However, they typically lack worst-case performance guarantees. To achieve the best of both worlds, we design a learning-augmented online algorithm that exhibits two desired properties: (i) consistency: closely approximating the optimal offline algorithm when the ML prediction is accurate and trusted; (ii) robustness: ensuring worst case performance guarantee even ML predictions are inaccurate. Finally, we perform extensive simulations to show that our online algorithm performs well empirically and that our learning augmented algorithm achieves both consistency and robustness. 

Tsunamigenic earthquakes pose considerable risks, both economically and socially, yet earthquake and tsunami hazard assessments are typically conducted separately. Earthquakes associated with unexpected tsunamis, such as the 2018 Mw  7.5 strike-slip Sulawesi earthquake, emphasize the need to study the tsunami potential of active submarine faults in different tectonic settings. Here, we investigate physics-based scenarios combining simulations of 3D earthquake dynamic rupture and seismic wave propagation with tsunami generation and propagation. We present time-dependent modeling of one-way linked and 3D fully coupled earthquakes and tsunamis for the ∼ 100 km long Húsavík–Flatey Fault Zone (HFFZ) in North Iceland. Our analysis shows that the HFFZ has the potential to generate sizable tsunamis. The six dynamic rupture models sourcing our tsunami scenarios vary regarding hypocenter location, spatiotemporal evolution, fault slip, and fault structure complexity but coincide with historical earthquake magnitudes. Earthquake dynamic rupture scenarios on a less segmented fault system, particularly with a hypocenter location in the eastern part of the fault system, have a larger potential for local tsunami generation. Here, dynamically evolving large shallow fault slip (∼ 8 m), near-surface rake rotation (± 20∘), and significant coseismic vertical displacements of the local bathymetry (± 1 m) facilitate strike-slip faulting tsunami generation. We model tsunami crest to trough differences (total wave heights) of up to ∼ 0.9 m near the town Ólafsfjörður. In contrast, none of our scenarios endanger the town of Akureyri, which is shielded by multiple reflections within the narrow Eyjafjörður bay and by Hrísey island. We compare the modeled one-way linked tsunami waveforms with simulation results using a 3D fully coupled approach. We find good agreement in the tsunami arrival times and location of maximum tsunami heights. While seismic waves result in transient motions of the sea surface and affect the ocean response, they do not appear to contribute to tsunami generation. However, complex source effects arise in the fully coupled simulations, such as tsunami dispersion effects and the complex superposition of seismic and acoustic waves within the shallow continental shelf of North Iceland. We find that the vertical velocity amplitudes of near-source acoustic waves are unexpectedly high – larger than those corresponding to the actual tsunami – which may serve as a rapid indicator of surface dynamic rupture. Our results have important implications for understanding the tsunamigenic potential of strike-slip fault systems worldwide and the coseismic acoustic wave excitation during tsunami generation and may help to inform future tsunami early warning systems.