9 papers
Wavelet Flow Matching for Multi-Scale Physics Emulation
Gabriele Accarino, Juan Nathaniel, Carla Roesch +4
Accurate emulation of multi-scale physical systems governed by PDEs demands models that remain stable over long autoregressive rollouts while preserving fine-scale structures. Dete…
Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning
Savannah L. Ferretti, Jerry Lin, Sara Shamekh +3
Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information across these dimensions in highly no…
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…
CuMoLoS-MAE: A Masked Autoencoder for Remote Sensing Data Reconstruction
Anurup Naskar, Nathanael Zhixin Wong, Sara Shamekh
Accurate atmospheric profiles from remote sensing instruments such as Doppler Lidar, Radar, and radiometers are frequently corrupted by low-SNR (Signal to Noise Ratio) gates, range…
WaveSim: A Wavelet-based Multi-scale Similarity Metric for Weather and Climate Fields
Gabriele Accarino, Viviana Acquaviva, Sara Shamekh +2
We introduce WaveSim, a multi-scale similarity metric for the evaluation of spatial fields in weather and climate applications. WaveSim exploits wavelet transforms to decompose inp…
Towards a Unified Data-Driven Boundary Layer Momentum Flux Parameterization for Ocean and Atmosphere
Renaud Falga, Sara Shamekh, Laure Zanna
Boundary layer turbulence, particularly the vertical fluxes of momentum, shapes the evolution of winds and currents and plays a critical role in weather, climate, and biogeochemica…