11 citations · 15 across the 12 of their papers we have counts for
13 papers
Accounting for model error in ensemble variational data assimilation through learned generative dynamics
Marc Bocquet, Tobias S. Finn, Sibo Cheng
Many approaches have recently been proposed to combine data assimilation with machine learning for improved forecasts with geophysical models. Here, we focus on combining advanced…
From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations
Abhishek A. Sabnis, Mihai Mitrea, Lya Lugon +5
Full-field reconstruction of air pollution is essential for evaluating pollution exposure and supporting public health decision-making. However, the complex interactions among poll…
Zero-shot generalization of transformer neural operators to larger domains
Armand de Villeroché, Sibo Cheng, Vincent Le Guen +5
Transformer-based neural operators have shown remarkable performance for approximating solution operators of partial differential equations on complex geometries. However, existing…
Anchored-Branched Steady-state WInd Flow Transformer (AB-SWIFT): a metamodel for 3D atmospheric flow in urban environments
Armand de Villeroché, Rem-Sophia Mouradi, Vincent Le Guen +5
Air flow modeling at a local scale is essential for applications such as pollutant dispersion modeling or wind farm modeling. To circumvent costly Computational Fluid Dynamics (CFD…
Hybrid physics-data-driven modeling for sea ice thermodynamics and transfer learning
Giovanni De Cillis, Alberto Carrassi, Julien Brajard +5
This study explores a physics-data driven hybrid approach for sea-ice column physics models, in which a machine learning (ML) component acts as a state-dependent parameterization o…
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…
Generative AI models capture realistic sea-ice evolution from days to decades
Tobias Sebastian Finn, Marc Bocquet, Pierre Rampal +4
Sea ice plays an important role in stabilising the Earth system. Yet, representing its dynamics remains a major challenge for models, as the underlying processes are scale-invarian…
A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model
Wenbo Yu, Anirbit Ghosh, Tobias Sebastian Finn +3
Thanks to recent advances in generative AI, computers can now simulate realistic and complex natural processes. We apply this capability to predict how wildfires spread, a task mad…
Ensemble Kalman filter in latent space using a variational autoencoder pair
Ivo Pasmans, Yumeng Chen, Tobias Sebastian Finn +2
Popular (ensemble) Kalman filter data assimilation (DA) approaches assume that the errors in both the a priori estimate of the state and those in the observations are Gaussian. For…
Machine learning for modelling unstructured grid data in computational physics: a review
Sibo Cheng, Marc Bocquet, Weiping Ding +20
Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for co…
Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble
Marc Bocquet, Alban Farchi, Tobias S. Finn +5
We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning. The focus is on learning the analysis step of sequential DA, fr…
Towards diffusion models for large-scale sea-ice modelling
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi +2
We make the first steps towards diffusion models for unconditional generation of multivariate and Arctic-wide sea-ice states. While targeting to reduce the computational costs by d…
Development of an offline and online hybrid model for the Integrated Forecasting System
Alban Farchi, Marcin Chrust, Marc Bocquet +1
In recent years, there has been significant progress in the development of fully data-driven global numerical weather prediction models. These machine learning weather prediction m…