Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics

By Sungyong Seo, Chuizheng Meng, Yan Liu. 2020

66 citations recorded by the source corpus.

Subfields: Graph Learning, AI for Physical Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In AI for Physical Sciences

1. Robert Brand
2. Philipp Holl
3. Yun Fei
4. Nils Thürey
5. Kiwon Um

Citations in the corpus

• Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, neutral): “PINNs have been extended to convolutional [340] and graph neural network [270] backbones.”
• Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers (2020, background): “, for rigid-body physics in the context of human reasoning [5, 64, 3] or weather predictions [51], particles are also a popular basis for fluid flow problems [33, 61, 48].”