arXiv Machine Learning
Efficient Real-Time Adaptation of ROMs for Unsteady Flows Using Data Assimilation
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arXiv Machine Learning
Sep 2
Efficient Adaptation of ROMs for Unsteady Flows Using Data Assimilation
The paper presents a lightweight retraining strategy for a parameterized Reduced Order Model (ROM) that achieves full‑model accuracy using only a fraction of the computational effort and sparse observations. The ROM architecture combines a Variational Autoencoder for dimensionality reduction with a transformer network that evolves latent states while accounting for the Reynolds number as an external control variable. By leveraging the probabilistic VAE, the method generates trajectory ensembles and uncertainty estimates, and adapts to out‑of‑sample parameters through sparse data assimilation with an ensemble Kalman filter, focusing retraining on the autoencoder to correct latent manifold distortions.
arXiv Machine Learning
Aug 13
Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates
arXiv:2608. 11435v1 Announce Type: new Abstract: Forward and inverse modeling of parametric dynamical systems requires surrogate models that are not only accurate for state prediction, but also informative for parameter calibration.
arXiv Machine Learning
Oct 3
AI Emulation of Stochastic Sudden Stratospheric Warming with Interpretable Latent Structure
The paper presents a probabilistic deep learning emulator—a ResNet‑inspired Conditional Variational Autoencoder—for the stochastic Holton–Mass model of stratospheric variability, which exhibits rare transitions between strong and weak polar vortex regimes. The emulator accurately reproduces short‑term dynamics, steady‑state distributions, regime persistence, rare transition rates, the committor function, and expected lead times. Analysis of the 32‑dimensional latent space via PCA reveals an unsupervised separation into four physically interpretable clusters that correspond to the two vortex regimes and their stable or transition‑prone states.
arXiv Machine Learning
Sep 4
SimCast-S2S: A Computationally Efficient Diffusion Model for Subseasonal Precipitation Forecasting
SimCast‑S2S is a generative latent‑diffusion framework designed for probabilistic subseasonal‑to‑seasonal precipitation forecasting. It tackles three key challenges: it uses a diffusion‑based generative pipeline for uncertainty quantification, operates in a compact latent space learned by VAEs for efficient large‑ensemble generation, and employs transfer learning with LoRA to overcome limited training data. On reanalysis data, it outperforms deep‑learning baselines and competes with or surpasses state‑of‑the‑art operational systems such as ECMWF‑S2S.
arXiv Machine Learning
Jul 28
The balance between compactness and forecast accuracy of data-driven latent-space reduced-order models in controlled wake flows
arXiv:2607. 24569v1 Announce Type: cross Abstract: Model-based active flow control requires predictive models that are accurate, stable, and fast enough for real-time optimisation.
arXiv Machine Learning
Aug 18
Iterative Refinement Diffusion for Super-Resolved Data Assimilation of Multiscale Physical Systems
arXiv:2608. 14744v1 Announce Type: new Abstract: Recovering high-resolution states from sparse, low-resolution observations is a central challenge in scientific machine learning and data assimilation.