Oh. just to be clear first, I’m not the OP. Sorry for the confusion.

I do understand your point, and I think it’s a fair one: when someone claims to "recreate" something, it really helps readers to know how close the result is to the original, especially for people who don’t already understand the domain.

I was mostly reacting to the idea that faithfulness always has to be the primary axis of evaluation. In practice, only a subset of users actually care about 100% fidelity. For example with DSP plugins or NES emulators, many people ultimately judge them by how they sound or feel, especially when the original artifact is aesthetic in nature.

My own case is a bit different, but related. Even though I’m working on a sensor, having a perfectly accurate physical model of the material is secondary to my actual goal. What I’m trying to produce is an end result composed of a printable geometry, a neural model to interpret it, and calibration procedures. The physics simulator is merely a tool, not a claim.

In fact, if I want the design to transfer well from simulation to reality, it probably makes more sense to intentionally train the model across multiple variations of the physics rather than betting everything on a single "accurate" simulator. That way, when confronted with the real world, adaptation becomes easier rather than harder.

So I fully agree that clarity about "how close" matters when that’s the claim. I’m just suggesting that in some projects, closeness to the original isn’t always the most informative metric.

One reason I find my case illuminating is that it makes the "what metric are we optimizing?" question very explicit.

Sure, I can report proxy metrics (e.g. prediction error between simulated vs measured deformation fields, contact localization error, force/pressure estimation error, sensitivity/resolution, robustness across hysteresis/creep and repeated cycles). Those are useful for debugging.

But the real metric is functional: can this cheap, printable sensor + model enable dexterous manipulation without vision – tasks where humans rely heavily on touch/proprioception, like closing a zipper or handling thin, finicky objects – without needing $500/sq-inch "microscope-like" tactile sensors (GelSight being the canonical example)?

If it gets anywhere close to that capability with commodity materials, then the project is a success, even if no single simulator configuration is "the" ground truth.

What could OP’s next move be? Designing and building their own circuit. Likewise, someone who built a NES emulator might eventually try designing their own console. It doesn’t feel that far-fetched.

Ah that makes more sense, I couldn't make the connection!

So on "So I fully agree that clarity about "how close" matters when that’s the claim. I’m just suggesting that in some projects, closeness to the original isn’t always the most informative metric." reminds me of https://en.wikipedia.org/wiki/Goodhart%27s_law

That being said as OP titled " I used AI to recreate X" then I expect I would still argue that the audience has now expectation that whatever OP created, regardless of why and how, should be relatively close to X. If people are expert on X then they can probably figure out quite quickly if it is for them "close enough" but for others it's very hard.

Ah that makes more sense, I couldn't make the connection!

So on "So I fully agree that clarity about "how close" matters when that’s the claim. I’m just suggesting that in some projects, closeness to the original isn’t always the most informative metric." reminds me of https://en.wikipedia.org/wiki/Goodhart%27s_law

That being said as OP titled " I used AI to recreate X" then I would still argue that the audience has now expectation that whatever OP created, regardless of why and how, should be relatively close to X. If people are expert on X then they can probably figure out quite quickly if it is for them "close enough" but for others it's very hard.