Bdtyktl

I have been reviewing the articles for various top-rank journals and conferences for the last few years. After all these experiences, I can tell you there is no way to confirm the correctness of simulation results. Therefore, I usually make comments on design, procedure, mathematical and analytical analysis.

In the results section, I can ask some questions about why this or that is so, but how can I judge if the simulation was really performed or these are just fabricated graphs?

Do you have reason to doubt their claims? Do they seem in some way unreasonable? If it isn't standard in your field to release code I don't think a reviewer should necessarily demand it, regardless of your feelings about making code public.

The authors should describe their methodology sufficiently for someone else to replicate it; in that way, they are putting their reputations at risk that were someone to duplicate their approach they would find the same results. Fabricating results is a very serious accusation. There are some statistical approaches to test whether data are likely to be fabricated, but the efficacy of this approach depends on the sophistication of the fabrication, and that question is better suited to CrossValidated.

If their work is meaningful in the field, at some point someone will implement their approach again. There is necessarily a bit of trust in science that people do what they say they've done.

After all these experiences, I can tell you there is no way to confirm the correctness of simulation results.

That is not necessarily true. In some cases, it is very easy to discern that a graph cannot possibly be correct or at the least has been badly misconstrued or misinterpreted. I had such a mistake caught in one of my early papers and have caught them in several papers I have reviewed.

It is not easy to prove that the simulations have actually been performed. However, the Open Science framework is designed to make it easier to verify results of both computational and experimental work.

there is no way to confirm the correctness of simulation results.

Simulations should be repeatable, hence, correctness can be checked by re-running the simulation. Of course, the authors might not provide the necessary code, but then you can request the code as a part of the review process.

You can't really judge if the simulation was really performed. That's why we've had things such as the Schön scandal - the reviewers of those manuscripts didn't detect the fraud either.

What you can do is implement the "smell test". Is this approach feasible? Are the results reasonable? Were there any glaring omissions? If you can't see any obvious problems with the simulation, that's good enough: the real peer review happens after publication.