@inproceedings{lange_optimised_2017,
	title = {Optimised finite difference computation from symbolic equations},
	url = {https://arxiv.org/abs/1707.03776v1},
	abstract = {Domain-specific high-productivity environments are playing an increasingly important role in scientific computing due to the levels of abstraction and automation they provide. In this paper we introduce Devito, an open-source domain-specific framework for solving partial differential equations from symbolic problem definitions by the finite difference method. We highlight the generation and automated execution of highly optimized stencil code from only a few lines of high-level symbolic Python for a set of scientific equations, before exploring the use of Devito operators in seismic inversion problems.},
	language = {en},
	booktitle = {Proceeding of the 15th {Python} in {Science} {Conference}},
	author = {Lange, Michael and Kukreja, Navjot and Luporitni, Fabio and Louboutin, Mathias and Yount, Charles and Hückelheim, Jan and Gorman, Gerard J.},
	month = jul,
	year = {2017},
	keywords = {finite difference method, partial differential equations, uses sympy},
}

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