07-11, 15:40–15:50 (Europe/Amsterdam), Function (4.1)
The growing availability of automatic differentiation (AD) tools promises numerous applications for enhancing large-scale scientific simulations, from sensitivity analysis to machine learning. However, the interplay of AD ("the gradient you get") and numerical analysis, implicit differentiation and physical symmetries ("the gradient you want") is nontrivial. I will briefly present such considerations on examples from materials science (density-functional theory) in DFTK.jl.
The growing availability of automatic differentiation (AD) tools promises numerous applications for enhancing large-scale scientific simulations, from sensitivity analysis to machine learning. However, the interplay of AD ("the gradient you get") and numerical analysis, implicit differentiation and physical symmetries ("the gradient you want") is nontrivial. I will briefly present such considerations on examples from materials science (density-functional theory) in DFTK.jl.
