Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Ferretti, Savannah L., Lin, Jerry, Shamekh, Sara, Baldwin, Jane W., Pritchard, Michael S., Beucler, Tom |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Stress-testing the coupled behavior of hybrid physics-machine learning climate simulations on an unseen, warmer climate
by: Lin, Jerry, et al.
Published: (2024)
by: Lin, Jerry, et al.
Published: (2024)
Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications
by: Beucler, Tom, et al.
Published: (2024)
by: Beucler, Tom, et al.
Published: (2024)
Lessons Learned: Reproducibility, Replicability, and When to Stop
by: Gomez, Milton S., et al.
Published: (2024)
by: Gomez, Milton S., et al.
Published: (2024)
Towards a Unified Data-Driven Boundary Layer Momentum Flux Parameterization for Ocean and Atmosphere
by: Falga, Renaud, et al.
Published: (2025)
by: Falga, Renaud, et al.
Published: (2025)
CuMoLoS-MAE: A Masked Autoencoder for Remote Sensing Data Reconstruction
by: Naskar, Anurup, et al.
Published: (2025)
by: Naskar, Anurup, et al.
Published: (2025)
Navigating the Noise: Bringing Clarity to ML Parameterization Design with O(100) Ensembles
by: Lin, Jerry, et al.
Published: (2023)
by: Lin, Jerry, et al.
Published: (2023)
Data-Driven Equation Discovery of a Cloud Cover Parameterization
by: Grundner, Arthur, et al.
Published: (2023)
by: Grundner, Arthur, et al.
Published: (2023)
From Winter Storm Thermodynamics to Wind Gust Extremes: Discovering Interpretable Equations from Data
by: Tam, Frederick Iat-Hin, et al.
Published: (2025)
by: Tam, Frederick Iat-Hin, et al.
Published: (2025)
Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models
by: Feldmann, Monika, et al.
Published: (2024)
by: Feldmann, Monika, et al.
Published: (2024)
Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties with Deep Learning Multi-Member and Stochastic Parameterizations
by: Behrens, Gunnar, et al.
Published: (2024)
by: Behrens, Gunnar, et al.
Published: (2024)
Causally-informed deep learning to improve climate models and projections
by: Iglesias-Suarez, Fernando, et al.
Published: (2023)
by: Iglesias-Suarez, Fernando, et al.
Published: (2023)
Machine Learning of Vertical Fluxes by Unresolved Midlatitude Mesoscale Processes
by: Ismaili, Erisa, et al.
Published: (2026)
by: Ismaili, Erisa, et al.
Published: (2026)
Calibrated Conformal Prediction Intervals for Microphysical Process Rates
by: Simm, Miriam, et al.
Published: (2026)
by: Simm, Miriam, et al.
Published: (2026)
Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models
by: Leclerc, Antoine, et al.
Published: (2025)
by: Leclerc, Antoine, et al.
Published: (2025)
Identifying Three-Dimensional Radiative Patterns Associated with Early Tropical Cyclone Intensification
by: Tam, Frederick Iat-Hin, et al.
Published: (2024)
by: Tam, Frederick Iat-Hin, et al.
Published: (2024)
Climate-Invariant Machine Learning
by: Beucler, Tom, et al.
Published: (2021)
by: Beucler, Tom, et al.
Published: (2021)
Investigating the Robustness of Extreme Precipitation Super-Resolution Across Climates
by: Largeau, Louise, et al.
Published: (2025)
by: Largeau, Louise, et al.
Published: (2025)
Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model
by: Heuer, Helge, et al.
Published: (2025)
by: Heuer, Helge, et al.
Published: (2025)
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
by: Yu, Sungduk, et al.
Published: (2023)
by: Yu, Sungduk, et al.
Published: (2023)
Dissipating the correlation smokescreen: Causal decomposition of the radiative effects of biomass burning aerosols over the South-East Atlantic
by: Fons, Emilie, et al.
Published: (2026)
by: Fons, Emilie, et al.
Published: (2026)
SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland
by: Assouline, Dan, et al.
Published: (2026)
by: Assouline, Dan, et al.
Published: (2026)
Reduced Cloud Cover Errors in a Hybrid AI-Climate Model Through Equation Discovery And Automatic Tuning
by: Grundner, Arthur, et al.
Published: (2025)
by: Grundner, Arthur, et al.
Published: (2025)
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations
by: Hu, Zeyuan, et al.
Published: (2024)
by: Hu, Zeyuan, et al.
Published: (2024)
Representing Subgrid-Scale Cloud Effects in a Radiation Parameterization using Machine Learning: MLe-radiation v1.0
by: Hafner, Katharina, et al.
Published: (2025)
by: Hafner, Katharina, et al.
Published: (2025)
Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators
by: Mahesh, Ankur, et al.
Published: (2026)
by: Mahesh, Ankur, et al.
Published: (2026)
Long-Range Distillation: Distilling 10,000 Years of Simulated Climate into Long Timestep AI Weather Models
by: Martin, Scott A., et al.
Published: (2025)
by: Martin, Scott A., et al.
Published: (2025)
Crowdsourcing the Frontier: Advancing Hybrid Physics-ML Climate Simulation via a $50,000 Kaggle Competition
by: Lin, Jerry, et al.
Published: (2025)
by: Lin, Jerry, et al.
Published: (2025)
Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model
by: Wang, Chenggong, et al.
Published: (2024)
by: Wang, Chenggong, et al.
Published: (2024)
Global Forecasting of Tropical Cyclone Intensity Using Neural Weather Models
by: Gomez, Milton, et al.
Published: (2025)
by: Gomez, Milton, et al.
Published: (2025)
Improving Typhoon Predictions by Integrating Data-Driven Machine Learning Models with Physics Models Based on the Spectral Nudging and Data Assimilation
by: Niu, Zeyi, et al.
Published: (2024)
by: Niu, Zeyi, et al.
Published: (2024)
Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators
by: Mahesh, Ankur, et al.
Published: (2024)
by: Mahesh, Ankur, et al.
Published: (2024)
Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators
by: Mahesh, Ankur, et al.
Published: (2024)
by: Mahesh, Ankur, et al.
Published: (2024)
Towards accurate extreme event likelihoods from diffusion model climate emulators
by: Manshausen, Peter, et al.
Published: (2026)
by: Manshausen, Peter, et al.
Published: (2026)
Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset
by: Yu, Sungduk, et al.
Published: (2023)
by: Yu, Sungduk, et al.
Published: (2023)
Machine Learning for Cloud Detection in IASI Measurements: A Data-Driven SVM Approach with Physical Constraints
by: Zugarini, Chiara, et al.
Published: (2025)
by: Zugarini, Chiara, et al.
Published: (2025)
Data Driven Deep Learning for Correcting Global Climate Model Projections of SST and DSL in the Bay of Bengal
by: Pasula, Abhishek, et al.
Published: (2025)
by: Pasula, Abhishek, et al.
Published: (2025)
Principled Operator Learning in Ocean Dynamics: The Role of Temporal Structure
by: Jahanmard, Vahidreza, et al.
Published: (2025)
by: Jahanmard, Vahidreza, et al.
Published: (2025)
Setting the Standard: Recommended Practices for Data Preprocessing in Data-Driven Climate Prediction
by: Furtado, Jason C., et al.
Published: (2025)
by: Furtado, Jason C., et al.
Published: (2025)
Using Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts
by: Toride, Kinya, et al.
Published: (2024)
by: Toride, Kinya, et al.
Published: (2024)
Flow Matching for Convective-Scale Precipitation Downscaling
by: Wetherell, Tom
Published: (2026)
by: Wetherell, Tom
Published: (2026)
Similar Items
-
Stress-testing the coupled behavior of hybrid physics-machine learning climate simulations on an unseen, warmer climate
by: Lin, Jerry, et al.
Published: (2024) -
Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications
by: Beucler, Tom, et al.
Published: (2024) -
Lessons Learned: Reproducibility, Replicability, and When to Stop
by: Gomez, Milton S., et al.
Published: (2024) -
Towards a Unified Data-Driven Boundary Layer Momentum Flux Parameterization for Ocean and Atmosphere
by: Falga, Renaud, et al.
Published: (2025) -
CuMoLoS-MAE: A Masked Autoencoder for Remote Sensing Data Reconstruction
by: Naskar, Anurup, et al.
Published: (2025)