57 citations · 82 across the 5 of their papers we have counts for
5 papers
Inferring Underwater Topography with FINN
Coşku Can Horuz, Matthias Karlbauer, Timothy Praditia +3
Spatiotemporal partial differential equations (PDEs) find extensive application across various scientific and engineering fields. While numerous models have emerged from both physi…
The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory
Sergey Oladyshkin, Timothy Praditia, Ilja Kröker +3
Artificial Intelligence and Machine learning have been widely used in various fields of mathematical computing, physical modeling, computational science, communication science, and…
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
Makoto Takamoto, Timothy Praditia, Raphael Leiteritz +4
Machine learning-based modeling of physical systems has experienced increased interest in recent years. Despite some impressive progress, there is still a lack of benchmarks for Sc…
Composing Partial Differential Equations with Physics-Aware Neural Networks
Matthias Karlbauer, Timothy Praditia, Sebastian Otte +3
We introduce a compositional physics-aware FInite volume Neural Network (FINN) for learning spatiotemporal advection-diffusion processes. FINN implements a new way of combining the…
Finite Volume Neural Network: Modeling Subsurface Contaminant Transport
Timothy Praditia, Matthias Karlbauer, Sebastian Otte +3
Data-driven modeling of spatiotemporal physical processes with general deep learning methods is a highly challenging task. It is further exacerbated by the limited availability of…