• DOI:10.1016/j.jcp.2019.01.036
Corpus ID: 52144927

Data-driven discovery of PDEs in complex datasets

@article{Berg2019DatadrivenDO,
  title={Data-driven discovery of PDEs in complex datasets},
  author={Jens Berg and K. Nystr{\"o}m},
  journal={ArXiv},
  year={2019},
  volume={abs/1808.10788}
}

Jens Berg, K. Nyström

Published 2019

Mathematics, Computer Science

ArXiv

Abstract Many processes in science and engineering can be described by partial differential equations (PDEs). Traditionally, PDEs are derived by considering first principles of physics to derive the relations between the involved physical quantities of interest. A different approach is to measure the quantities of interest and use deep learning to reverse engineer the PDEs which are describing the physical process. In this paper we use machine learning, and deep learning in particular, to… CONTINUE READING

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