We introduce a compositional physics-aware FInite volume Neural Network (FINN) for
learning spatiotemporal advection-diffusion processes. FINN implements a new way of …

Improved understanding of complex hydrosystem processes is key to advance water
resources research. Nevertheless, the conventional way of modeling these processes …

Many physical processes such as weather phenomena or fluid mechanics are governed by
partial differential equations (PDEs). Modelling such dynamical systems using Neural …

The finite volume neural network (FINN) is an exception among recent physics-aware neural
network models as it allows the specification of arbitrary boundary conditions (BCs). FINN …

When modeling physical processes in spatially confined domains, the boundaries require
distinct consideration through specifying appropriate boundary conditions (BCs). The finite …

Spatiotemporal partial differential equations (PDEs) find extensive application across
various scientific and engineering fields. While numerous models have emerged from both …

Modeling data obtained from dynamical systems has gained attention in recent years as a
challenging task for machine learning models. Previous approaches assume the …

Abstract The novel DISTributed Artificial neural Network Architecture (DISTANA) is a
generative, recurrent graph convolution neural network. It implements a grid or mesh of …

Abstract Knowledge about the hidden factors that determine particular system dynamics is
crucial for both explaining them and pursuing goal-directed interventions. Inferring these …

Knowledge about the hidden factors that determine particular system dynamics is crucial for
both explaining them and pursuing goal-directed interventions. Inferring these factors from …