#automaticdifferentiation — Public Fediverse posts

`Various software efforts embrace the idea that object oriented programming enables a convenient implementation of the chain rule, facilitating so-called automatic differentiation via backpropagation. Such frameworks have no mechanism for simplifying the expressions (obtained via the chain rule) before evaluating them. As we illustrate below, the resulting errors tend to be unbounded.`

https://arxiv.org/abs/2305.03863

#calculus #software #numericalAnalysis #automaticDifferentiation #uncertainty

New publication https://doi.org/10.1038/s41524-025-01880-3

Our work on AD-DFPT, a unification of #automaticdifferentiation with linear response for #densityfunctionaltheory is published in npj Computational Materials. We show examples for #property predition, #uncertainty propagation, the design of #materials and #machinelearning of new #dft models. #condensedmatter #dftk

Automatic Differentiation Can Be Incorrect

https://www.stochasticlifestyle.com/the-numerical-analysis-of-differentiable-simulation-automatic-differentiation-can-be-incorrect/

#HackerNews #AutomaticDifferentiation #Incorrectness #NumericalAnalysis #Simulation #MachineLearning

New preprint: https://arxiv.org/abs/2509.07785

We present an implementation of AD-DFPT, a unification of #automaticdifferentiation with classical #dfpt response techniques for #densityfunctionaltheory (#dft). We demonstrate its use for #property predition, #uncertainty propagation, design of new #materials as well as the #machinelearning of new #dft models.

#condensedmatter #planewave #response #physics #simulation #computation

Have you ever thought 💡 of using JAX as 🧮 #automaticdifferentiation engine in 💻 finite element simulations? Boost the performance 🏇 of computationally-expensive hyperelastic material models with #jit in 🔍 FElupe! 🚀 🚀

https://github.com/adtzlr/felupe

#python #jax #finiteelementmethod #scientificcomputing #computationalmechanics #fea #fem #hyperelasticity