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Differentiable modelling to unify machine learning and physical models for geosciences
Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
Big Data in Earth system science and progress towards a digital twin
The concept of a digital twin of Earth envisages the convergence of Big Earth Data with
physics-based models in an interactive computational framework that enables monitoring …
physics-based models in an interactive computational framework that enables monitoring …
Deep learning in hydrology and water resources disciplines: Concepts, methods, applications, and research directions
Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …
recognition within the field of hydrology and water resources applications. Beginning with a …
Pushing the frontiers in climate modelling and analysis with machine learning
Climate modelling and analysis are facing new demands to enhance projections and
climate information. Here we argue that now is the time to push the frontiers of machine …
climate information. Here we argue that now is the time to push the frontiers of machine …
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
Recently, the advent of deep learning has spurred interest in the development of physics-
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …
A comprehensive review of deep learning applications in hydrology and water resources
M Sit, BZ Demiray, Z ** of soil organic carbon using machine learning algorithms in Northern Iran
Estimation of the soil organic carbon (SOC) content is of utmost importance in understanding
the chemical, physical, and biological functions of the soil. This study proposes machine …
the chemical, physical, and biological functions of the soil. This study proposes machine …
Uniting remote sensing, crop modelling and economics for agricultural risk management
The increasing availability of satellite data at higher spatial, temporal and spectral
resolutions is enabling new applications in agriculture and economic development …
resolutions is enabling new applications in agriculture and economic development …