no code implementations • 6 Mar 2024 • Alban Farchi, Marcin Chrust, Marc Bocquet, Massimo Bonavita
In this article, we propose to develop a model error correction for the operational Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts using a neural network.
no code implementations • 18 Mar 2023 • Sibo Cheng, Cesar Quilodran-Casas, Said Ouala, Alban Farchi, Che Liu, Pierre Tandeo, Ronan Fablet, Didier Lucor, Bertrand Iooss, Julien Brajard, Dunhui Xiao, Tijana Janjic, Weiping Ding, Yike Guo, Alberto Carrassi, Marc Bocquet, Rossella Arcucci
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.
no code implementations • 25 Oct 2022 • Alban Farchi, Marcin Chrust, Marc Bocquet, Patrick Laloyaux, Massimo Bonavita
Data assimilation is used to estimate the system state from the observations, while machine learning computes a surrogate model of the dynamical system based on those estimated states.
no code implementations • 21 Mar 2022 • Nikhil Garg, Ismael Balafrej, Terrence C. Stewart, Jean Michel Portal, Marc Bocquet, Damien Querlioz, Dominique Drouin, Jean Rouat, Yann Beilliard, Fabien Alibart
To validate the system-level performance of VDSP, we train a single-layer spiking neural network (SNN) for the recognition of handwritten digits.
no code implementations • 23 Jul 2021 • Quentin Malartic, Alban Farchi, Marc Bocquet
It features both local domains and covariance localisation in order to learn the chaotic dynamics and the local forcings.
no code implementations • 23 Jul 2021 • Alban Farchi, Marc Bocquet, Patrick Laloyaux, Massimo Bonavita, Quentin Malartic
We compare online and offline learning using the same framework with the two-scale Lorenz system, and show that with online learning, it is possible to extract all the information from sparse and noisy observations.
no code implementations • 2 Jul 2021 • Atreya Majumdar, Marc Bocquet, Tifenn Hirtzlin, Axel Laborieux, Jacques-Olivier Klein, Etienne Nowak, Elisa Vianello, Jean-Michel Portal, Damien Querlioz
However, the resistive change behavior in this regime suffers many fluctuations and is particularly challenging to model, especially in a way compatible with tools used for simulating deep learning.
no code implementations • 23 Oct 2020 • Alban Farchi, Patrick Laloyaux, Massimo Bonavita, Marc Bocquet
This yields a class of iterative methods in which, at each iteration a DA step assimilates the observations, and alternates with a ML step to learn the underlying dynamics of the DA analysis.
no code implementations • 9 Sep 2020 • Julien Brajard, Alberto Carrassi, Marc Bocquet, Laurent Bertino
Moreover, the attractor of the system is significantly better represented by the hybrid model than by the truncated model.
no code implementations • 20 Jun 2020 • Bogdan Penkovsky, Marc Bocquet, Tifenn Hirtzlin, Jacques-Olivier Klein, Etienne Nowak, Elisa Vianello, Jean-Michel Portal, Damien Querlioz
With new memory technology available, emerging Binarized Neural Networks (BNNs) are promising to reduce the energy impact of the forthcoming machine learning hardware generation, enabling machine learning on the edge devices and avoiding data transfer over the network.
no code implementations • 6 Jun 2020 • Marc Bocquet, Alban Farchi, Quentin Malartic
The reconstruction of the dynamics of an observed physical system as a surrogate model has been brought to the fore by recent advances in machine learning.
no code implementations • 17 Jan 2020 • Marc Bocquet, Julien Brajard, Alberto Carrassi, Laurent Bertino
The reconstruction from observations of high-dimensional chaotic dynamics such as geophysical flows is hampered by (i) the partial and noisy observations that can realistically be obtained, (ii) the need to learn from long time series of data, and (iii) the unstable nature of the dynamics.
no code implementations • 6 Jan 2020 • Julien Brajard, Alberto Carassi, Marc Bocquet, Laurent Bertino
The output analysis is spatially complete and is used as a training set by the neural network to update the surrogate model.
no code implementations • 12 Aug 2019 • Tifenn Hirtzlin, Bogdan Penkovsky, Jacques-Olivier Klein, Nicolas Locatelli, Adrien F. Vincent, Marc Bocquet, Jean-Michel Portal, Damien Querlioz
One of the most exciting applications of Spin Torque Magnetoresistive Random Access Memory (ST-MRAM) is the in-memory implementation of deep neural networks, which could allow improving the energy efficiency of Artificial Intelligence by orders of magnitude with regards to its implementation on computers and graphics cards.
1 code implementation • 3 Jun 2019 • Tifenn Hirtzlin, Bogdan Penkovsky, Marc Bocquet, Jacques-Olivier Klein, Jean-Michel Portal, Damien Querlioz
In this work, we propose a stochastic computing version of Binarized Neural Networks, where the input is also binarized.
Emerging Technologies