Accurate battery life prediction is a critical part of the business case for electric vehicles,
stationary energy storage, and nascent applications such as electric aircraft. Existing …

Drying is a complex process of simultaneous heat, mass, and momentum transport
phenomena with continuous phase changes. Numerical modelling is one of the most …

Gradient-based inverse design in photonics has already achieved remarkable results in
designing small-footprint, high-performance optical devices. The adjoint variable method …

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 …

The use of machine learning based on neural networks for cloud microphysical
parameterizations is investigated. As an example, we use the warm‐rain formation by …

We have developed a globally applicable diagnostic COVID-19 model by augmenting the
classical SIR epidemiological model with a neural network module. Our model does not rely …

Summary Global Sensitivity Analysis (GSA) methods are used to quantify the uncertainty in
the output of a model with respect to the parameters. These methods allow practitioners to …

Recent advances in differentiable modeling, a genre of physics-informed machine learning
that trains neural networks (NNs) together with process-based equations, have shown …

In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide
application range, from production machinery via automated vehicles to robots. CLCSs …