English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Solving large complex partial differential equations (PDEs), such as thosethat arise in computational fluid dynamics (CFD), is a computationallyexpensive process. This has motivated the use of deep learning approaches toapproximate the PDE solutions, yet the simulation results predicted from theseapproaches typically do not generalize well to truly novel scenarios. In thiswork, we develop a hybrid (graph) neural network that combines a traditionalgraph convolutional network with an embedded differentiable fluid dynamicssimulator inside the network itself. By combining an actual CFD simulator (runon a much coarser resolution representation of the problem) with the graphnetwork, we show that we can both generalize well to new situations and benefitfrom the substantial speedup of neural network CFD predictions, while alsosubstantially outperforming the coarse CFD simulation alone.

Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction