Cover Image

Extrapolability Improvement of Machine Learning-Based Evapotranspiration Models via Domain-Adversarial Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Shi, Haiyang
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Geophysics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Potential of Domain Adaptation in Machine Learning in Ecology and Hydrology to Improve Model Extrapolability
by: Shi, Haiyang
Published: (2024)

Approaches for enhancing extrapolability in process-based and data-driven models in hydrology
by: Shi, Haiyang
Published: (2024)

Broadband Ground Motion Synthesis via Generative Adversarial Neural Operators: Development and Validation
by: Shi, Yaozhong, et al.
Published: (2023)

The Physics-Informed Neural Network Gravity Model: Generation III
by: Martin, John, et al.
Published: (2023)

An End-to-End Differentiable, Graph Neural Network-Embedded Pore Network Model for Permeability Prediction
by: Zhao, Qingqi, et al.
Published: (2025)

Data-Driven Dynamic Friction Models based on Recurrent Neural Networks
by: Cortes, Gaëtan, et al.
Published: (2024)

A Test-Time Learning Approach to Reparameterize the Geophysical Inverse Problem with a Convolutional Neural Network
by: Xu, Anran, et al.
Published: (2023)

Double Difference Earthquake Location with Graph Neural Networks
by: McBrearty, Ian W., et al.
Published: (2024)

Towards Understanding the Benefits of Neural Network Parameterizations in Geophysical Inversions: A Study With Neural Fields
by: Xu, Anran, et al.
Published: (2025)

Multi-Constitutive Neural Network for Large Deformation Poromechanics Problem
by: Zhang, Qi, et al.
Published: (2020)

Thermal Earth Model for the Conterminous United States Using an Interpolative Physics-Informed Graph Neural Network (InterPIGNN)
by: Aljubran, Mohammad J., et al.
Published: (2024)

Physics-Informed Neural Networks for Predicting Hydrogen Sorption in Geological Formations: Thermodynamically Constrained Deep Learning Integrating Classical Adsorption Theory
by: Nooraiepour, Mohammad, et al.
Published: (2026)

Predicting the Geothermal Gradient in Colombia: a Machine Learning Approach
by: Mejía-Fragoso, Juan Camilo, et al.
Published: (2024)

Path Signatures and Graph Neural Networks for Slow Earthquake Analysis: Better Together?
by: Riess, Hans, et al.
Published: (2024)

Gabor-Enhanced Physics-Informed Neural Networks for Fast Simulations of Acoustic Wavefields
by: Abedi, Mohammad Mahdi, et al.
Published: (2025)

Machine Learning Algorithms for Transplanting Accelerometer Observations in Future Satellite Gravimetry Missions
by: Romeshkani, Mohsen, et al.
Published: (2025)

SeisRDT: Latent Diffusion Model Based On Representation Learning For Seismic Data Interpolation And Reconstruction
by: Wang, Shuang, et al.
Published: (2025)

Transcending Classical Neural Network Boundaries: A Quantum-Classical Synergistic Paradigm for Seismic Data Processing
by: Yuan, Zhengyi, et al.
Published: (2026)

Data-Driven and Theory-Guided Pseudo-Spectral Seismic Imaging Using Deep Neural Network Architectures
by: Zerafa, Christopher
Published: (2025)

Parameter-Efficient Transfer Learning for Microseismic Phase Picking Using a Neural Operator
by: Abdullin, Ayrat, et al.
Published: (2025)

EFKAN: A KAN-Integrated Neural Operator For Efficient Magnetotelluric Forward Modeling
by: Wang, Feng, et al.
Published: (2025)

Physics-Trained Neural Network as Inverse Problem Solver for Potential Fields: An Example of Downward Continuation between Arbitrary Surfaces
by: Sun, Jing, et al.
Published: (2025)

Direct Estimation of Porosity from Seismic Data using Rock and Wave Physics Informed Neural Networks (RW-PINN)
by: Vashisth, Divakar, et al.
Published: (2022)

Graph Network Surrogate Model for Subsurface Flow Optimization
by: Tang, Haoyu, et al.
Published: (2023)

Fourier Neural Operator Surrogate Model to Predict 3D Seismic Waves Propagation
by: Lehmann, Fanny, et al.
Published: (2023)

Learning Generative Models for Lumped Rainfall-Runoff Modeling
by: Yang, Yang, et al.
Published: (2023)

Domain-informed explainable boosting machines for trustworthy lateral spread predictions
by: Hsiao, Cheng-Hsi, et al.
Published: (2026)

Enforcing Reciprocity in Operator Learning for Seismic Wave Propagation
by: Zou, Caifeng, et al.
Published: (2026)

Fully Data-Driven Model for Increasing Sampling Rate Frequency of Seismic Data using Super-Resolution Generative Adversarial Networks
by: Gholizadeh, Navid, et al.
Published: (2024)

Toward Routing River Water in Land Surface Models with Recurrent Neural Networks
by: Lima, Mauricio, et al.
Published: (2024)

Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations
by: González-Santana, Alejandro J., et al.
Published: (2026)

Diffusion Model-Based Posterior Sampling in Full Waveform Inversion
by: Taufik, Mohammad H., et al.
Published: (2025)

Scientific Machine Learning Seismology
by: Okazaki, Tomohisa
Published: (2024)

Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization?
by: Chen, Ruihua, et al.
Published: (2025)

Ambient Noise Full Waveform Inversion with Neural Operators
by: Zou, Caifeng, et al.
Published: (2025)

Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling
by: Wei, Xiaoli, et al.
Published: (2023)

Machine learning enhanced data assimilation framework for multiscale carbonate rock characterization
by: Bo, Zhenkai, et al.
Published: (2026)

PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency
by: Dong, Xintong, et al.
Published: (2025)

WaveCastNet: Rapid Wavefield Forecasting for Earthquake Early Warning via Deep Sequence to Sequence Learning
by: Lyu, Dongwei, et al.
Published: (2024)

Short-term Streamflow and Flood Forecasting based on Graph Convolutional Recurrent Neural Network and Residual Error Learning
by: Pan, Xiyu, et al.
Published: (2024)