As a genre of physics-informed machine learning, differentiable process-based hydrologic
models (abbreviated as δ or delta models) with regionalized deep-network-based …

Earth system models (ESMs) are the primary tools for investigating future Earth system
states at timescales from decades to centuries, especially in response to anthropogenic …

Deep learning methods have frequently outperformed conceptual hydrologic models in
rainfall-runoff modelling. Attempts of investigating such deep learning models internally are …

Summary The Chemical Master Equation (CME) provides an accurate description of
stochastic biochemical reaction networks in well-mixed conditions, but it cannot be solved …

As a genre of physics-informed machine learning, differentiable process-based hydrologic
models (abbreviated as δ or delta models) with regionalized deep-network-based …

Photosynthesis plays an important role in carbon, nitrogen, and water cycles. Ecosystem
models for photosynthesis are characterized by many parameters that are obtained from …

The term NeuralODE describes the structural combination of an Artificial Neural Network
(ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as …

Geoscientific models are facing increasing challenges to exploit growing datasets coming
from remote sensing. Universal Differential Equations (UDEs), aided by differentiable …

Artificial intelligence (AI) is delivering major advances in the construction engineering sector
in this era of building information modelling, applying data collection techniques based on …

Quantum algorithms are an area of intensive research thanks to their potential for speeding
up certain specific tasks exponentially. However, for the time being, high error rates on the …