At some point the llm ingested a few open source NES emulators and many articles on their architecture. So i question the llm creativity involved with these types examples. Probably also for dsps.

I’m not claiming a 100% faithful physical recreation in the strict scientific sense.

If you look at my other comment in this thread, my project is about designing proprioceptive touch sensors (robot skin) using a soft-body simulator largely built with the help of an AI. At this stage, absolute physical accuracy isn’t really the point. By design, the system already includes a neural model in the loop (via EIT), so the notion of "accuracy" is ultimately evaluated through that learned representation rather than against raw physical equations alone.

What I need instead is a model that is faithful to my constraints: very cheap, easily accessible materials, with properties that are usually considered undesirable for sensing: instability, high hysteresis, low gauge factor. My bet is that these constraints can be compensated for by a more circular system design, where the geometry of the sensor is optimized to work with them.

Bridging the gap to reality is intentionally simple: 3D-print whatever geometry the simulator converges to, run the same strain/stress tests on the physical samples, and use that data to fine-tune the sensor model.

Since everything is ultimately interpreted through a neural network, some physical imprecision upstream may actually be acceptable, or even beneficial, if it makes the eventual transfer and fine-tuning on real-world data easier.