Research Highlights

Prediction-to-Mitigation with Digital Twins of the Earth’s Weather

The project focuses on analysing and forecasting extreme weather events under climate change by blending dynamical …

Discipline-Informed Neural Networks for Interpretable Time-Series Discovery

This project develops post-hoc interpretability tools for neural networks applied to time series and sequential data.

REBOT – Rethinking Underwater Robot Manipulation

REBOT explores octopus-inspired manipulation strategies in aquatic environments through modeling, simulation, and …

Data-driven modeling and Explainable AI

We are developing fast forecasting tools for multivariate time series modeling and forecasting, combining data compression techniques with neural networks and interpretability methods.

Prediction-to-Mitigation with Digital Twins of the Earth’s Weather

Ongoing

The project focuses on analysing and forecasting extreme weather events under climate change by blending dynamical system theory and deep learning technologies. It also aims to assess damages and inform mitigation strategies.

Publications

A. Gualandi, D. Faranda, C. Marone, M. Cocco, G. Mengaldo, Deterministic and stochastic chaos characterize laboratory earthquakes, Earth and Planetary Science Letters (2023).
C. Duong, V. C. Raghuram, A. Lee, R. Mao, G. Mengaldo, E. Cambria, Neurosymbolic AI for mining public opinions about wildfires, Cognitive Computation (2023).

Collaborators

NUS

ECMWF

Argonne

CNRS

ENS

Cambridge

People

Zhou

Chenyu

Xin