CRPS-LAM: Regional ensemble weather forecasting from matching marginals

A recent study has focused on training large language models (LLMs) to adhere to medical consensus guidelines in their reasoning and prediction processes. This approach aims to enhance the accuracy and trustworthiness of LLMs in medical applications, addressing a critical gap in the field where explanations for predictions have often been overlooked.

A new computational framework has been introduced that integrates physics-informed machine learning (ML) to develop a continuous cooling transformation (CCT) model for steel. This model, trained on a dataset of 4,100 diagrams, aims to enhance the understanding of the relationship between chemical composition, processing parameters, and resulting microstructure and properties of steel.

A new study published on arXiv presents a novel approach to probabilistic wind power forecasting that addresses the issue of missing data, which often arises from sensor faults or communication outages. The method proposes a joint generative model that learns from incomplete data, allowing for forecasts that marginalize unobserved features rather than relying on imputation techniques.

A recent study has introduced a distribution-informed learning framework to enhance geophysical predictions, particularly in complex dynamical systems like climate processes. This approach leverages prior knowledge of marginal distributions to complement short-term observations, addressing challenges posed by sparse data and the sensitive nature of these systems.