Despite the sophistication of global climate models (GCMs), their coarse spatial resolution
limits their ability to resolve important aspects of climate variability and change at the local …

We introduce a generative learning framework to model high-dimensional parametric
systems using gradient guidance and virtual observations. We consider systems described …

Eddy-resolving turbulence simulations are essential for understanding and controlling
complex unsteady fluid dynamics, with significant implications for engineering and scientific …

Turbulent flows, characterized by their chaotic and stochastic nature, have historically
presented formidable challenges to predictive computational modeling. Traditional eddy …

We make the first steps towards diffusion models for unconditional generation of multivariate
and Arctic-wide sea-ice states. While targeting to reduce the computational costs by diffusion …

We present a method to downscale idealized geophysical fluid simulations using generative
models based on diffusion maps. By analyzing the Fourier spectra of fields drawn from …

We introduce deep generative diffusion for multivariate and regional surrogate modeling
learned from sea‐ice simulations. Given initial conditions and atmospheric forcings, the …

Chaotic systems, such as turbulent flows, are ubiquitous in science and engineering.
However, their study remains a challenge due to the large range scales, and the strong …

Data-driven deep learning models are on the verge of transforming global weather
forecasting. It is an open question if this success can extend to climate modeling, where long …

Regional high-resolution climate projections are crucial for many applications, such as
agriculture, hydrology, and natural hazard risk assessment. Dynamical downscaling, the …