Closure problems are omnipresent when simulating multiscale systems, where some
quantities and processes cannot be fully prescribed despite their effects on the simulation's …

Floods, caused by intense rainfall or typhoons, overwhelming urban drainage systems, pose
significant threats to urban areas, leading to substantial economic losses and endangering …

As applications of machine learning proliferate, innovative algorithms inspired by specific
real-world challenges have become increasingly important. Such work offers the potential …

We present the design and scalable implementation of an exascale climate emulator for
addressing the escalating computational and storage requirements of high-resolution Earth …

Protein language models trained on evolutionary data have emerged as powerful tools for
predictive problems involving protein sequence, structure, and function. However, these …

In this position paper, we argue that application-driven research has been systemically
under-valued in the machine learning community. As applications of machine learning …

This work integrates machine learning into an atmospheric parameterization to target
uncertain mixing processes while maintaining interpretable, predictive, and well‐established …

Conventional notions of generalization often fail to describe the ability of learned models to
capture meaningful information from dynamical data. A neural network that learns complex …

An enhanced understanding of the mechanisms responsible for wind turbine blade leading-
edge erosion (LEE) and advancing technology readiness level (TRL) solutions for …

Since the inception of our planet, the meteorological environment, as reflected through
spatio-temporal data, has always been a fundamental factor influencing human life, socio …