A taxonomy of automatic differentiation pitfalls
Sign Up to like & getrecommendations! Published in 2024 at "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"
DOI: 10.1002/widm.1555
Abstract: Automatic differentiation is a popular technique for computing derivatives of computer programs. While automatic differentiation has been successfully used in countless engineering, science, and machine learning applications, it can sometimes nevertheless produce surprising results. In… read more here.
Keywords: differentiation; automatic differentiation; taxonomy automatic; differentiation pitfalls ... See more keywords
Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
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DOI: 10.1007/s10915-025-02965-3
Abstract: Neural network-based approaches have recently shown significant promise in solving partial differential equations (PDEs) in science and engineering, especially in scenarios featuring complex domains or incorporation of empirical data. One advantage of the neural network… read more here.
Keywords: training neural; automatic differentiation; differential equations; neural networks ... See more keywords
Optimised flow control based on automatic differentiation in compressible turbulent channel flows
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DOI: 10.1017/jfm.2025.304
Abstract: Abstract This study presents an automatic differentiation (AD)-based optimisation framework for flow control in compressible turbulent channel flows. Using a differentiable solver, JAX-Fluids, we designed fully differentiable boundary conditions that allow for the precise calculation… read more here.
Keywords: control; flow control; automatic differentiation; channel ... See more keywords
Perturbation confusion in forward automatic differentiation of higher-order functions
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DOI: 10.1017/s095679681900008x
Abstract: Abstract Automatic differentiation (AD) is a technique for augmenting computer programs to compute derivatives. The essence of AD in its forward accumulation mode is to attach perturbations to each number, and propagate these through the… read more here.
Keywords: order; order functions; automatic differentiation; higher order ... See more keywords
Automatic Differentiation for Explicitly Correlated MP2
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DOI: 10.1021/acs.jctc.4c00818
Abstract: Automatic differentiation (AD) offers a route to achieve arbitrary-order derivatives of challenging wave function methods without the use of analytic gradients or response theory. Currently, AD has been predominantly used in methods where first- and/or… read more here.
Keywords: mp2 f12; differentiation explicitly; correlated mp2; explicitly correlated ... See more keywords
Automatic Differentiation in Quantum Chemistry with Applications to Fully Variational Hartree–Fock
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DOI: 10.1021/acscentsci.7b00586
Abstract: Automatic differentiation (AD) is a powerful tool that allows calculating derivatives of implemented algorithms with respect to all of their parameters up to machine precision, without the need to explicitly add any additional functions. Thus,… read more here.
Keywords: hartree fock; quantum chemistry; chemistry; automatic differentiation ... See more keywords
Discovering optimal kinetic pathways for self-assembly using automatic differentiation
Sign Up to like & getrecommendations! Published in 2024 at "Proceedings of the National Academy of Sciences of the United States of America"
DOI: 10.1073/pnas.2403384121
Abstract: Significance Self-assembly of protein subunits into macromolecular complexes is ubiquitous and essential for living systems. A common obstacle during self-assembly is the formation of kinetically trapped intermediates that dramatically reduce functional yield. We show here… read more here.
Keywords: optimal kinetic; automatic differentiation; self assembly; self ... See more keywords
Automatic Differentiation in MetaPhysicL and Its Applications in MOOSE
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DOI: 10.1080/00295450.2020.1838877
Abstract: Abstract Efficient solution via Newton’s method of nonlinear systems of equations requires an accurate representation of the Jacobian, corresponding to the derivatives of the component residual equations with respect to the degrees of freedom. In… read more here.
Keywords: metaphysicl applications; differentiation metaphysicl; applications moose; automatic differentiation ... See more keywords
Automatic differentiation of a finite-volume-based transient heat conduction code for sensitivity analysis
Sign Up to like & getrecommendations! Published in 2018 at "Numerical Heat Transfer, Part B: Fundamentals"
DOI: 10.1080/10407790.2018.1486648
Abstract: Abstract A general method for computing derivatives of solution fields and other simulation outputs, with respect to arbitrary input quantities, is proposed. The method of automatic differentiation is used to carry out differentiation and propagate… read more here.
Keywords: differentiation finite; code; finite volume; automatic differentiation ... See more keywords
Mixed-language automatic differentiation
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DOI: 10.1080/10556788.2018.1435650
Abstract: As Automatic Differentiation (AD) usage is spreading to larger and more sophisticated applications, problems arise for codes that use several programming languages. This work describes the issues involved in interoperability between languages and focuses on… read more here.
Keywords: mixed language; language automatic; language; automatic differentiation ... See more keywords
Accelerating Optimal Power Flow With Structure-Aware Automatic Differentiation and Code Generation
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DOI: 10.1109/tpwrs.2024.3483489
Abstract: This letter proposes a structure-aware automatic differentiation method to accelerate the solution of alternating current optimal power flow (ACOPF) with nonlinear programming (NLP) solvers. By exploiting the isomorphic structure of nonlinear power flow constraints in… read more here.
Keywords: automatic differentiation; structure; power; power flow ... See more keywords