7 papers
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…
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…
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…