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We are excited to announce the release of Sim4Life V9.0. Now powered by o²S²PARC technology, Sim4Life amplifies its industry-leading simulation capabilities for design and optimization – from body-mounted wireless devices to medical technology and basic research, setting benchmarks for user experience and third-party extensibility. [image: 1752499181982-2506_s4l_v9.gif] What’s new: Unified Ecosystem: Thanks to full incorporation of o²S²PARC technology, the unified ecosystem allows straightforward integration of user and third-party tools, making Sim4Life an easily extendable, universal simulation platform and enabling the development of advanced, shareable workflows. New Plugin Framework: Third-party solvers can now be integrated, as demonstrated by the beta integration of the ray-tracing engine SionnaRT and the FEniCS FEM simulator. Meta-Modeling Preview: Parameterized models can now be intelligently and efficiently explored, e.g., using response surfaces and multi-goal optimization. Cloud-Powered: Users can tap into high-performance CPU/GPU cloud resources directly from the desktop, and monitor simulations in real time. Fully Shareable: Projects are easily and safely shared with coworkers. Learn more about the release here. Access it via sim4life.io, sim4life.science, or sim4life-lite.io (free for students), or download the latest desktop version from our website. A comprehensive list of Sim4Life V9.0’s new features, improvements, and fixes (web and desktop versions) is provided in our Release Notes. Please send your feedback and suggestions to s4l-support@zmt.swiss.

When the stop button is pushed in the task manager, while a simulation is running, it will generate an event that is equivalent to "enforcing" a "convergence reached" state from the solver perspective. That's why the following log will appear inside the Solver-Log tab WARNING: [...] Simulation end request received. The solvers starts to consider this. Steady state detected at iteration x, remaining time steps are y. Simulation performed z iterations. Elapsed time for 'Time Update' was xx:xx:xx wall clock time.

I would suggest first cloning the nerve trajectory, and then discretizing it. The result is an unlocked axon model. You can then delete the nerve trajectory clone if needed.