11 citations · 15 across the 12 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…