Nonlinear dynamical systems


We are developing methods to identify the predictability of complex systems. The methods are rooted in dynamical system and extreme value theory. These are to be used in conjunction with forecasting methods, both traditional and modern, to improve their perfomance. Current application areas include extreme weather and earthquake modeling and prediction.

Collaborations


Argonne National Laboratory (USA)   European Center for Medium-Range Weather Forecasts   University of Cambridge (UK)   University of Geneva (Switzerland)  

Sponsors




Awards
22-4900-A0001-0
22-5191-A0001-0


Awards
22-3565-A0001-1



Lorenz attractor colored by newly developed indices.

Data-driven predictive modeling


We are developing fast forecasting tools for multivariate time series modeling and forecasting. This involve blending data compression techniques, such as autoencoders and spectral proper orthogonal decomposition, with neural networks. The former are used to identify a reduced manifold to decrease the dimension of the problem. The latter are used to forecast the system in the reduced manifold. Given the 'black-box' nature of neural-network approaches, we are developing interpretability methods, to allow for more 'white-box' forecasting workflows. Current application areas include extreme weather prediction, climate resilience and sustaninability, and healthcare.

Collaborations


Argonne National Laboratory (USA)   Scuola Superiore Sant'Anna (Italy)   Scuola Internazionale Superiore di Studi Avanzati (Italy)   University of Cambridge (UK)   University of Geneva (Switzerland)  

Sponsors




Awards
22-4900-A0001-0
22-5191-A0001-0


Awards
22-3565-A0001-1



Atmospheric mode identified using PySPOD for period = 5 days, using ERA5 reanalysis data

Partial differential equations and numerical analysis


We are using numerical analysis to develop high-fidelity simulation tools for multiscale, multi-physics problems governed by partial differential equations. The main focus is on spectral element methods, with applications in engineering flow simulation, and soft-robotics multi-physics modeling.

Collaborations


Imperial College London (UK)   KAUST (Saudi Arabia)   University of Toronto (Canada)   University of Waterloo (Canada)  

Sponsors




Awards
22-4900-A0001-0
22-5191-A0001-0


Awards
22-3565-A0001-1



Flow past a high-performance road car obtained with Nektar++.