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==Applications==
Differentiable programming has been applied in areas such as combining [[deep learning]] with [[physics engines]] in [[robotics]],<ref>{{cite arXiv |eprint=1611.01652 |class=cs.NE |first1=Jonas |last1=Degrave |first2=Michiel |last2=Hermans |title=A Differentiable Physics Engine for Deep Learning in Robotics |date=2016-11-05 |last3=Dambre |first3=Joni |last4=wyffels |first4=Francis}}</ref> solving electronic structure problems with differentiable [[density functional theory]],<ref name="Li2021">{{cite journal |last1=Li |first1=Li |last2=Hoyer |first2=Stephan |last3=Pederson |first3=Ryan |last4=Sun |first4=Ruoxi |last5=Cubuk |first5=Ekin D. |last6=Riley |first6=Patrick |last7=Burke |first7=Kieron |year=2021 |title=Kohn-Sham Equations as Regularizer: Building Prior Knowledge into Machine-Learned Physics |journal=Physical Review Letters |volume=126 |issue=3 |pages=036401 |arxiv=2009.08551 |bibcode=2021PhRvL.126c6401L |doi=10.1103/PhysRevLett.126.036401 |pmid=33543980 |doi-access=free}}</ref> differentiable [[Ray tracing (graphics)|ray tracing]],<ref>{{cite journal |first1=Tzu-Mao |last1=Li |first2=Miika |last2=Aittala |first3=Frédo |last3=Durand |first4=Jaakko |last4=Lehtinen |title=Differentiable Monte Carlo Ray Tracing through Edge Sampling |journal=ACM Transactions on Graphics |volume=37 |issue=6 |pages=222:1–11 |date=2018 |doi=10.1145/3272127.3275109 |s2cid=52839714 |url=https://people.csail.mit.edu/tzumao/diffrt/|doi-access=free }}</ref> [[image processing]],<ref>{{cite journal |first1=Tzu-Mao |last1=Li |first2=Michaël |last2=Gharbi |first3=Andrew |last3=Adams |first4=Frédo |last4=Durand |first5=Jonathan |last5=Ragan-Kelley |title=Differentiable Programming for Image Processing and Deep Learning in Halide |journal=ACM Transactions on Graphics |volume=37 |issue=4 |pages=139:1–13 |date=August 2018 |doi=10.1145/3197517.3201383 |s2cid=46927588 |url=https://cseweb.ucsd.edu/~tzli/gradient_halide|doi-access=free }}</ref> and [[probabilistic programming]].<ref name="diffprog-zygote"/>
==See also==
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