1 citations · 2 across the 10 of their papers we have counts for
4 papers · 1 filter
: Operator-based Mixture Ensemble for Generative Assimilation
Pouria Behnoudfar, Nan Chen
Characterizing non-Gaussian posterior distributions in partially observed high-dimensional nonlinear systems remains a fundamental challenge in data assimilation. Ensemble Kalman f…
Bridging Idealized and Operational Models: An Explainable AI Framework for Earth System Emulators
Pouria Behnoudfar, Charlotte Moser, Marc Bocquet +2
Computer models are indispensable tools for understanding the Earth system. While high-resolution operational models have achieved many successes, they exhibit persistent biases, p…
RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles
Pouria Behnoudfar, Nan Chen
Machine learning has become a powerful tool for enhancing data assimilation. While supervised learning remains the standard method, reinforcement learning (RL) offers unique advant…
Physics-informed Spectral Learning: the Discrete Helmholtz--Hodge Decomposition
Luis Espath, Pouria Behnoudfar, Raul Tempone
In this work, we further develop the Physics-informed Spectral Learning (PiSL) by Espath et al. \cite{Esp21} based on a discrete projection to solve the discrete Hodge--Helmh…