In recent years, we have witnessed a significant shift toward ever-more complex and ever-
larger-scale systems in the majority of the grand societal challenges tackled in applied …

Extreme events gain the attention of researchers due to their utmost importance in various
contexts ranging from climate to brain. An observable that deviates significantly from its long …

Extreme events in fluid flows, waves, or structures interacting with them are critical for a wide
range of areas, including reliability and design in engineering, as well as modeling risk of …

Extreme events and the related anomalous statistics are ubiquitously observed in many
natural systems, and the development of efficient methods to understand and accurately …

Diffusion models are a class of probabilistic generative models that have been widely used
as a prior for image processing tasks like text conditional generation and inpainting. We …

Understanding and predicting uncertain things are the central themes of scientific evolution.
Human beings revolve around these fears of uncertainties concerning various aspects like a …

Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution
which is challenging to map on a global scale. Here, we present a data-driven ozone …

The forecasting of high-dimensional, spatiotemporal nonlinear systems has made
tremendous progress with the advent of model-free machine learning techniques. However …

The remarkable flexibility and adaptability of both deep learning models and ensemble
methods have led to the proliferation for their application in understanding many physical …

Diverse collective dynamics emerge in dynamical systems interacting on top of complex
network architectures. Along this line of research, temporal network has come out to be one …