Search Results for author: Marc Bocquet

Found 15 papers, 1 papers with code

Online model error correction with neural networks: application to the Integrated Forecasting System

no code implementations6 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.

Online model error correction with neural networks in the incremental 4D-Var framework

no code implementations25 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.

State, global and local parameter estimation using local ensemble Kalman filters: applications to online machine learning of chaotic dynamics

no code implementations23 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.

A comparison of combined data assimilation and machine learning methods for offline and online model error correction

no code implementations23 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.

Model of the Weak Reset Process in HfOx Resistive Memory for Deep Learning Frameworks

no code implementations2 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.

Handwritten Digit Recognition

Using machine learning to correct model error in data assimilation and forecast applications

no code implementations23 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.

BIG-bench Machine Learning

Combining data assimilation and machine learning to infer unresolved scale parametrisation

no code implementations9 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.

BIG-bench Machine Learning

In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications

no code implementations20 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.

BIG-bench Machine Learning

Online learning of both state and dynamics using ensemble Kalman filters

no code implementations6 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.

BIG-bench Machine Learning Time Series +1

Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization

no code implementations17 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.

Bayesian Inference BIG-bench Machine Learning +2

Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction

no code implementations12 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.

Stochastic Computing for Hardware Implementation of Binarized Neural Networks

1 code implementation3 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

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