#AutomaticDifferentiation

CryoJAX is a cryo-EM image-simulation library for building computational data-analysis applications in JAX #CryoEM #ImageSimulation #AutomaticDifferentiation doi.org/10.1107/S205...

The adoption of differentiable modeling and simulation has gained significant traction in computational science and engineering (CSE) workflows, driving advancements in optimization, sensitivity analysis, uncertainty quantification, model discovery, and more. This minisymposium brings together scientists, researchers, practitioners, students, and software developers who are either utilizing or developing differentiable computational models. We also welcome those seeking inspiration to integrate these methodologies into their own work. Our goal is to provide a platform for knowledge exchange and learning from each other's experiences—highlighting both success stories and challenges encountered along the way. We invite contributions that share experiences in developing differentiable computational models—including specific challenges faced, innovative solutions found—and applications of these models across various domains. Attendees can look forward to learning from notable contributors within the Julia community who will present innovative use cases while fostering collaboration among participants. Through networking opportunities and shared insights, we aim to inspire both seasoned users and newcomers by demonstrating how differentiable modelling can enhance model-based CSE workflows!

Alan Correa and Sarah Williamsom will host a minisymposium on "Differentiable Computational Models and their Applications" at JuliaCon 2026. Find out more at pretalx.com/juliacon-202... and submit your talk at juliacon.org/2026/cfp

#julialang #automaticdifferentiation

Variational optimization of projected entangled-pair states on the triangular lattice.

journals.aps.org/prb/abstract...

The “meta-message” is that the use of tools such as #automaticdifferentiation, and the co-design of the ansatz class and the physical system can improve #tensornetwork methods.

#SciML fact of the day: automatic differentiation fails to give the correct derivative on a lot of very simple functions 😱 😱 😱 . #julialang #automaticdifferentiation

youtube.com/shorts/KTguZ...

Shadowing Methods for Forward and Adjoint Sensitivity Analysis of Chaotic Systems | FS In this post, we dig into sensitivity analysis of chaotic systems. Chaotic systems are dynamical, deterministic systems that are extremely sensitive to small changes in the initial state or the system...

Other work that we have done has highlighted that #automaticdifferentiation calculates incorrect derivatives on chaotic systems (frankschae.github.io/post/shadowi...).