• DOI:10.1145/3394486.3403198
Corpus ID: 208176379

Towards Physics-informed Deep Learning for Turbulent Flow Prediction

@article{Wang2020TowardsPD,
  title={Towards Physics-informed Deep Learning for Turbulent Flow Prediction},
  author={Rui Wang and K. Kashinath and M. Mustafa and Adrian Albert and R. Yu},
  journal={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
  year={2020}
}

Rui Wang, K. Kashinath, +2 authors R. Yu

Published 2020

Computer Science, Physics, Mathematics

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this paper, we aim to predict turbulent flow by learning its highly nonlinear dynamics from spatiotemporal velocity fields of large-scale fluid flow simulations of relevance to turbulence modeling and climate modeling. We adopt a hybrid approach by marrying two well… CONTINUE READING

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