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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 |last1=Degrave |first1=Jonas |last2=Hermans |first2=Michiel |last3=Dambre |first3=Joni |last4=wyffels |first4=Francis |title=A Differentiable Physics Engine for Deep Learning in Robotics |date=2016 |class=cs.NE }}</ref> solving [[Quantum chemistry#Electronic structure|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> [[differentiable imaging]],<ref>{{Cite journal |last=Chen |first=Ni |last2=Cao |first2=Liangcai |last3=Poon |first3=Ting‐Chung |last4=Lee |first4=Byoungho |last5=Lam |first5=Edmund Y. |title=Differentiable Imaging: A New Tool for Computational Optical Imaging |url=https://onlinelibrary.wiley.com/doi/10.1002/apxr.202200118 |journal=Advanced Physics Research |language=en |volume=2 |issue=6 |doi=10.1002/apxr.202200118 |issn=2751-1200|doi-access=free |hdl=10754/686576 |hdl-access=free }}</ref><ref>{{Cite journal |last=Chen |first=Ni |last2=Brady |first2=David J. |last3=Lam |first3=Edmund Y. |date=2025-07 |title=Differentiable Imaging: Progress, Challenges, and Outlook |url=https://spj.science.org/doi/10.34133/adi.0117 |journal=Advanced Devices & Instrumentation |language=en |volume=6 |doi=10.34133/adi.0117 |issn=2767-9713}}</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 |hdl=1721.1/122623 |hdl-access=free }}</ref> and [[probabilistic programming]].<ref name="diffprog-zygote"/>
==Multidisciplinary application==
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