Learning Variational Data Assimilation Models and Solvers

Data-driven discovery of coordinates and governing equations

(2019) Kathleen Champion et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods

(2018) Alice Lucas et al.   IEEE SIGNAL PROCESSING MAGAZINE

Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature

(2018) Said Ouala et al.   Remote Sensing

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

DeepISP: Toward Learning an End-to-End Image Processing Pipeline

(2018) Eli Schwartz et al.   IEEE TRANSACTIONS ON IMAGE PROCESSING

Convolutional Neural Networks for Inverse Problems in Imaging: A Review

(2017) Michael T. McCann et al.   IEEE SIGNAL PROCESSING MAGAZINE

The Analog Data Assimilation

(2017) Redouane Lguensat et al.   MONTHLY WEATHER REVIEW

A review of operational methods of variational and ensemble-variational data assimilation

(2017) R. N. Bannister   QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

(2016) Steven L. Brunton et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Deep learning

(2015) Yann LeCun et al.   NATURE