5 citations · 10 across the 18 of their papers we have counts for
10 papers · 1 filter
LO-SDA: Latent Optimization for Score-based Atmospheric Data Assimilation
Jing-An Sun, Hang Fan, Junchao Gong +8
Data assimilation (DA) plays a pivotal role in numerical weather prediction by systematically integrating sparse observations with model forecasts to estimate optimal atmospheric i…
Learning more physically realistic dynamics in machine-learning based weather forecasting with latent-space constraints
Hang Fan, Yi Xiao, Yongquan Qu +5
Data-driven machine learning (ML) models are reshaping weather forecasting and have shown the potential to accelerate and surpass traditional physics-based approaches, leading to a…
PnP-DA: Towards Principled Plug-and-Play Integration of Variational Data Assimilation and Generative Models
Yongquan Qu, Matthieu Blanke, Sara Shamekh +1
Earth system modeling presents a fundamental challenge in scientific computing: capturing complex, multiscale nonlinear dynamics in computationally efficient models while minimizin…
JAX-LaB: A High-Performance, Differentiable, Lattice Boltzmann Library for Modeling Multiphase Fluid Dynamics in Geosciences and Engineering
Piyush Pradhan, Pierre Gentine, Shaina Kelly
We introduce JAX-LaB, a differentiable, Python-based Lattice Boltzmann simulation library designed for modeling multiphase and multiphysics fluid dynamics problems in hydrologic, g…
Align-DA: Align Score-based Atmospheric Data Assimilation with Multiple Preferences
Jing-An Sun, Hang Fan, Junchao Gong +8
Data assimilation (DA) aims to estimate the full state of a dynamical system by combining partial and noisy observations with a prior model forecast, commonly referred to as the ba…
Deep Koopman operator framework for causal discovery in nonlinear dynamical systems
Juan Nathaniel, Carla Roesch, Jatan Buch +4
We use a deep Koopman operator-theoretic formalism to develop a novel causal discovery algorithm, Kausal. Causal discovery aims to identify cause-effect mechanisms for better scien…
Strictly Constrained Generative Modeling via Split Augmented Langevin Sampling
Matthieu Blanke, Yongquan Qu, Sara Shamekh +1
Deep generative models hold great promise for representing complex physical systems, but their deployment is currently limited by the lack of guarantees on the physical plausibilit…
CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models
Benjamin Herdeanu, Juan Nathaniel, Carla Roesch +4
Causal discovery for dynamical systems poses a major challenge in fields where active interventions are infeasible. Most methods used to investigate these systems and their associa…
Generative emulation of chaotic dynamics with coherent prior
Juan Nathaniel, Pierre Gentine
Data-driven emulation of nonlinear dynamics is challenging due to long-range skill decay that often produces physically unrealistic outputs. Recent advances in generative modeling…
Physically Consistent Global Atmospheric Data Assimilation with Machine Learning in Latent Space
Hang Fan, Lei Bai, Ben Fei +6
Data assimilation (DA) integrates observations with model forecasts to produce optimized atmospheric states, whose physical consistency is critical for stable weather forecasting a…