32 citations · 87 across the 14 of their papers we have counts for
7 papers · 1 filter
Learning Inter-Annual Flood Loss Risk Models From Historical Flood Insurance Claims and Extreme Rainfall Data
Joaquin Salas, Anamitra Saha, Sai Ravela
Flooding is one of the most disastrous natural hazards, responsible for substantial economic losses. A predictive model for flood-induced financial damages is useful for many appli…
Learn Like The Pro: Norms from Theory to Size Neural Computation
Margaret Trautner, Ziwei Li, Sai Ravela
The optimal design of neural networks is a critical problem in many applications. Here, we investigate how dynamical systems with polynomial nonlinearities can inform the design of…
Informative Neural Ensemble Kalman Learning
Margaret Trautner, Gabriel Margolis, Sai Ravela
In stochastic systems, informative approaches select key measurement or decision variables that maximize information gain to enhance the efficacy of model-related inferences. Neura…
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling
Nishant Yadav, Sai Ravela, Auroop R. Ganguly
Systems exhibiting nonlinear dynamics, including but not limited to chaos, are ubiquitous across Earth Sciences such as Meteorology, Hydrology, Climate and Ecology, as well as Biol…
Neural Networks as Geometric Chaotic Maps
Ziwei Li, Sai Ravela
The use of artificial neural networks as models of chaotic dynamics has been rapidly expanding. Still, a theoretical understanding of how neural networks learn chaos is lacking. He…
Neural Integration of Continuous Dynamics
Margaret Trautner, Sai Ravela
Neural dynamical systems are dynamical systems that are described at least in part by neural networks. The class of continuous-time neural dynamical systems must, however, be numer…
Machine Learning for the Geosciences: Challenges and Opportunities
Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela +2
Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data,…