The book "System Archetype Basics" studies 7 troublesome patterns. Here's one.

As the symptom of some problem increases, we work harder at the "fundamental solution" but also the quicker and easier "symptomatic solution". Both of these decrease the symptom of the problem. But the fundamental solution takes longer - there's a delay. Also, the symptomatic solution tends to cause a side-effect that tends to work against the fundamental solution.

The picture here is called a 'causal loop diagram' because it helps you spot feedback loops. Just find a loop in the picture and multiply the plus and minus signs around it to tell if it's a positive or a negative feedback loop!

The math of these things runs deeper than you might think. I'm talking about it at a category theory workshop in Glasgow on Monday June 2nd. It's at 2 pm UK time, and you can watch it on Zoom if you register here:

https://jademaster.xyz/TACT25.html

This workshop is being run by my former student @JadeMasterMath and it's at the University of Strathclyde, Royal College, room RC 512. My long-time n-Category Cafe co-host, the philosopher David Corfield, will be giving a talk on how modalities in homotopy type theory can be thought of as 'logical', and my friend Nathaniel Osgood will be talking about compositional modeling in public health. There are also lots of other great talks. Abstracts are here:

https://jademaster.xyz/ListOfAbstracts

and a tentative schedule is here:

https://jademaster.xyz/TACTSchedule.html

I hope to see you there, at least virtually!

Atrial constitutive neural networks

Mathias Peirlinck, Kevin Linka, Ellen Kuhl
https://arxiv.org/abs/2504.02748 https://arxiv.org/pdf/2504.02748 https://arxiv.org/html/2504.02748

arXiv:2504.02748v1 Announce Type: new
Abstract: This work presents a novel approach for characterizing the mechanical behavior of atrial tissue using constitutive neural networks. Based on experimental biaxial tensile test data of healthy human atria, we automatically discover the most appropriate constitutive material model, thereby overcoming the limitations of traditional, pre-defined models. This approach offers a new perspective on modeling atrial mechanics and is a significant step towards improved simulation and prediction of cardiac health.