We discuss the emerging advances and opportunities at the intersection of machine
learning (ML) and climate physics, highlighting the use of ML techniques, including …

Earth system models have been continously improved over the past decades, but systematic
errors compared with observations and uncertainties in climate projections remain. This is …

The scarcity of data presents a critical obstacle to the efficacy of medical vision-language pre-
training (VLP). A potential solution lies in the combination of datasets from various language …

High-dimensional dynamical systems often require computationally intensive physics-based
simulations, making full physical space data assimilation impractical. Latent data …

This study investigates the integration of machine learning (ML) and data assimilation (DA)
techniques, focusing on implementing surrogate models for Geological Carbon Storage …

Abstract Variational Data Assimilation (DA) has been broadly used in engineering problems
for field reconstruction and prediction by performing a weighted combination of multiple …

Scientific communities are still motivated to create novel approaches and methodologies for
early estimation of software project development efforts and testing efforts in soft computing …

The rise of machine learning (ML) has significantly advanced the field of coastal
oceanography. This review aims to examine the existing deficiencies in numerical …

The weather forecasting system is important for science and society, and significant
achievements have been made in applying artificial intelligence (AI) to medium-range …

The ocean is a crucial component of the Earth's system. It profoundly influences human
activities and plays a critical role in climate regulation. Our understanding has significantly …