78 citations · 184 across the 26 of their papers we have counts for
47 papers
Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation
Gérôme Andry, Sacha Lewin, François Rozet +6
Deep learning has advanced weather forecasting, but accurate predictions first require identifying the current state of the atmosphere from observational data. In this work, we int…
An implementation of neural simulation-based inference for parameter estimation in ATLAS
ATLAS Collaboration
Neural simulation-based inference is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation witho…
Simulation-Based Inference Benchmark for Weak Lensing Cosmology
Justine Zeghal, Denise Lanzieri, François Lanusse +5
Standard cosmological analysis, which relies on two-point statistics, fails to extract the full information of the data. This limits our ability to constrain with precision cosmolo…
Robust Ocean Subgrid-Scale Parameterizations Using Fourier Neural Operators
Victor Mangeleer, Gilles Louppe
In climate simulations, small-scale processes shape ocean dynamics but remain computationally expensive to resolve directly. For this reason, their contributions are commonly appro…
Score-based Data Assimilation for a Two-Layer Quasi-Geostrophic Model
François Rozet, Gilles Louppe
Data assimilation addresses the problem of identifying plausible state trajectories of dynamical systems given noisy or incomplete observations. In geosciences, it presents challen…
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability
Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh +4
Bayesian inference allows expressing the uncertainty of posterior belief under a probabilistic model given prior information and the likelihood of the evidence. Predominantly, the…