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Differentiable programming has been applied in areas such as combining [[deep learning]] with [[physics engines]] in [[robotics]],<ref>{{cite preprint |arxiv=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 }}</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"/>
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Differentiable programming is making significant strides in various fields beyond its traditional applications. In healthcare and life sciences, for example, it's being used for deep learning in biophysics-based modelling of molecular mechanisms. This involves leveraging differentiable programming in areas such as protein structure prediction and drug discovery. These applications demonstrate the potential of differentiable programming in contributing to significant advancements in understanding complex biological systems and improving healthcare solutions.<ref>{{cite journal |last1=AlQuraishi |first1=Mohammed |last2=Sorger |first2=Peter K. |title=Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms |journal=Nature Methods |date=October 2021 |volume=18 |issue=10 |pages=1169–1180 |doi=10.1038/s41592-021-01283-4 |pmid=34608321 |pmc=8793939 }}</ref>
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