Differentiable programming has been applied in areas such as combining [[deep learning]] with [[physics engines]] in [[robotics]], solving electronic structure problems with differentiable [[density functional theory]], differentiable [[Ray tracing (graphics)|ray tracing]], [[image processing]], and [[probabilistic programming]].<ref>{{cite arXiv|last1=Degrave|first1=Jonas|last2=Hermans|first2=Michiel|last3=Dambre|first3=Joni|last4=wyffels|first4=Francis|date=2016-11-05|title=A Differentiable Physics Engine for Deep Learning in Robotics|eprint=1611.01652|class=cs.NE}}</ref><ref name='Li2021'>{{cite journal |title=Kohn-Sham Equations as Regularizer: Building Prior Knowledge into Machine-Learned Physics |journal=Physical Review Letters |year=2021 |first1=Li |last1=Li | first2=Stephan | last2=Hoyer | first3=Ryan | last3=Pederson | first4=Ruoxi | last4=Sun | first5=Ekin D. | last5=Cubuk | first6=Patrick | last6=Riley |first7=Kieron | last7=Burke |volume=126 |issue=3 |pages=036401 |doi=10.1103/PhysRevLett.126.036401 |pmid=33543980 |arxiv=2009.08551 |bibcode=2021PhRvL.126c6401L |doi-access=free}}</ref><ref>{{Cite web|url=https://people.csail.mit.edu/tzumao/diffrt/|title=Differentiable Monte Carlo Ray Tracing through Edge Sampling|website=people.csail.mit.edu|access-date=2019-02-13}}</ref><ref>{{Cite web|url=https://sciml.ai/roadmap/|title=SciML Scientific Machine Learning Open Source Software Organization Roadmap|website=sciml.ai|access-date=2020-07-19}}</ref><ref>{{Cite web|url=https://people.csail.mit.edu/tzumao/gradient_halide/|title=Differentiable Programming for Image Processing and Deep Learning in Halide|website=people.csail.mit.edu|access-date=2019-02-13}}</ref><ref name="diffprog-zygote"/>, and [[Time series|time-series]] analysis <ref>https://arxiv.org/abs/2207.03577</ref>.
