Image de couverture de livre

Knowledge-Guided Machine Learning Models to Upscale Evapotranspiration in the U.S. Midwest

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Rozanov, Aleksei, Subedi, Samikshya, Sharma, Vasudha, Runck, Bryan C.
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

CarbonBench: A Global Benchmark for Upscaling of Carbon Fluxes Using Zero-Shot Learning
par: Rozanov, Aleksei, et autres
Publié: (2026)

Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes
par: Rozanov, Aleksei, et autres
Publié: (2026)

Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm
par: Rozanov, Nikolai
Publié: (2024)

Extrapolability Improvement of Machine Learning-Based Evapotranspiration Models via Domain-Adversarial Neural Networks
par: Shi, Haiyang
Publié: (2024)

Spatial Distribution-Shift Aware Knowledge-Guided Machine Learning
par: Sharma, Arun, et autres
Publié: (2025)

Algorithms and Scientific Software for Quasi-Monte Carlo, Fast Gaussian Process Regression, and Scientific Machine Learning
par: Sorokin, Aleksei G.
Publié: (2025)

Heterogeneous Federated Learning with Prototype Alignment and Upscaling
par: Lee, Gyuejeong, et autres
Publié: (2025)

Emotional EEG Classification using Upscaled Connectivity Matrices
par: Lee, Chae-Won, et autres
Publié: (2025)

Operator Learning: A Statistical Perspective
par: Subedi, Unique, et autres
Publié: (2025)

On the Benefits of Active Data Collection in Operator Learning
par: Subedi, Unique, et autres
Publié: (2024)

Interpretable Machine Learning for Reservoir Water Temperatures in the U.S. Red River Basin of the South
par: Suaza-Sierra, Isabela, et autres
Publié: (2025)

Predicting VBAC Outcomes from U.S. Natality Data using Deep and Classical Machine Learning Models
par: Anand, Ananya
Publié: (2025)

A Gradient Boosted Mixed-Model Machine Learning Framework for Vessel Speed in the U.S. Arctic
par: Pant, Mauli, et autres
Publié: (2026)

Towards Fine-Tuning-Based Site Calibration for Knowledge-Guided Machine Learning: A Summary of Results
par: Zeng, Ruolei, et autres
Publié: (2025)

Multi-Criteria Comparison as a Method of Advancing Knowledge-Guided Machine Learning
par: Harman, Jason L., et autres
Publié: (2024)

From Specification to Architecture: A Theory Compiler for Knowledge-Guided Machine Learning
par: Hevapathige, Asela, et autres
Publié: (2026)

Limits To (Machine) Learning
par: Chen, Zhimin, et autres
Publié: (2025)

SHAP-Guided Regularization in Machine Learning Models
par: Saadallah, Amal
Publié: (2025)

Is Zero-Shot Super-Resolution Possible in Operator Learning?
par: Subedi, Unique, et autres
Publié: (2026)

Online Learning with Set-Valued Feedback
par: Raman, Vinod, et autres
Publié: (2023)

Empirical Upscaling of Point-scale Soil Moisture Measurements for Spatial Evaluation of Model Simulations and Satellite Retrievals
par: Yu, Yi, et autres
Publié: (2024)

Scientific Knowledge-Guided Machine Learning for Vessel Power Prediction: A Comparative Study
par: Bourchas, Orfeas, et autres
Publié: (2026)

Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators
par: Subedi, Unique, et autres
Publié: (2024)

Online Infinite-Dimensional Regression: Learning Linear Operators
par: Raman, Vinod, et autres
Publié: (2023)

The Biosecurity Blind Spot: Systematic Dual-use Detection in Open Science Infrastructure
par: Sharma, Vasudha, et autres
Publié: (2026)

Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time Generalization
par: Patel, Yash, et autres
Publié: (2025)

Knowledge-Guided Machine Learning for Stabilizing Near-Shortest Path Routing
par: Chen, Yung-Fu, et autres
Publié: (2025)

Investigating U.S. Consumer Demand for Food Products with Innovative Transportation Certificates Based on Stated Preferences and Machine Learning Approaches
par: Bi, Jingchen, et autres
Publié: (2025)

Convolutional Surrogate for 3D Discrete Fracture-Matrix Tensor Upscaling
par: Špetlík, Martin, et autres
Publié: (2026)

Impact of Employing Weather Forecast Data as Input to the Estimation of Evapotranspiration by Deep Neural Network Models
par: Vaz, Pedro J., et autres
Publié: (2024)

Multiclass Transductive Online Learning
par: Hanneke, Steve, et autres
Publié: (2024)

StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking
par: Rozanov, Nikolai, et autres
Publié: (2024)

Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generation
par: Somerstep, Seamus, et autres
Publié: (2025)

Informed Machine Learning with Knowledge Landmarks
par: Dai, Chuyi, et autres
Publié: (2026)

Explainable Federated Learning for U.S. State-Level Financial Distress Modeling
par: Carta, Lorenzo, et autres
Publié: (2025)

Hourly Short Term Load Forecasting for Residential Buildings and Energy Communities
par: Kychkin, Aleksei, et autres
Publié: (2025)

Improving Generalization Performance of YOLOv8 for Camera Trap Object Detection
par: Subedi, Aroj
Publié: (2024)

How to Upscale Neural Networks with Scaling Law? A Survey and Practical Guidelines
par: Sengupta, Ayan, et autres
Publié: (2025)

Replication Study: Enhancing Hydrological Modeling with Physics-Guided Machine Learning
par: Esmaeilzadeh, Mostafa, et autres
Publié: (2024)

Comparing Targeting Strategies for Maximizing Social Welfare with Limited Resources
par: Sharma, Vibhhu, et autres
Publié: (2024)