arXiv:2609.13514v1 Announce Type: new Abstract: Ensuring the reliability of black-box machine learning models in safety-critical space missions remains a significant challenge, particularly when grou...
arXiv:2606. 23766v1 Announce Type: cross Abstract: The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the upcoming Ariel mission.
arXiv:2607. 08581v1 Announce Type: new Abstract: Extreme Learning Machine (ELM) computes output weights analytically using the Moore-Penrose pseudoinverse.
arXiv:2508. 10120v2 Announce Type: replace-cross Abstract: Cloud detection is fundamental for the interpretation and operational exploitation of hyperspectral infrared sounders, yet the capability of infrared radiances alone to provide reliable cloud information remains insufficiently assessed.
arXiv:2606. 02886v1 Announce Type: cross Abstract: Deep learning weather models now match numerical weather prediction accuracy while running orders of magnitude faster, but produce deterministic forecasts without uncertainty estimates, a critical gap for high-stakes decisions during extreme weather events.
arXiv:2409. 08958v3 Announce Type: replace-cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful deep learning approach for solving partial differential equations (PDEs) in the physical sciences, yet their behavior remains largely opaque and is typically understood through failure mode analyses rather than explicit interpretability.
Learning functional relationships from noisy data is a central problem in scientific inference. Spectral methods approximate unknown functions by expanding them in a basis and estimating the corresponding coefficients from data, but the stability of these coefficients under noise remains poorly understood.
arXiv:2510. 22517v3 Announce Type: replace-cross Abstract: Optimal sensor placement (OSP) is critical for efficient, accurate monitoring, control, and inference in complex physical systems.
arXiv:2609.13659v1 Announce Type: cross Abstract: Underwater Acoustic Target Recognition (UATR) of ships is well-suited for machine learning, yet its progress is hindered by the lack of large, divers...
arXiv:2606. 08563v1 Announce Type: new Abstract: While global data-driven models excel at predicting continuous atmospheric variables, three-dimensional hydrometeor forecasting remains challenging due to the zero-inflated, long-tailed distributions of these variables.
arXiv:2609.06173v1 Announce Type: new Abstract: Post-deployment drift poses a critical risk to algorithmic accountability, particularly when ground truth labels are delayed and performance degradatio...
arXiv:2607. 09371v1 Announce Type: cross Abstract: Flexible machine-learning methods can be sensitive to hidden confounding: they may learn associations induced by unobserved confounders rather than stable signals.