Research dependent on future discovery? I work in a field of data-driven modeling of physical systems (think simulating weather patterns, particle interations, solid structures, etc.). As the name implies, producing a good model depends on access to high-quality, or "high-fidelity" data that it taken to be the truth, or a fairly accurate representation of nature or an experiment. However, the system I am trying to model is one which nobody can consistently produce simulations that exactly match experiments. This is not a huge problem for me, as I simply use the best simulations that reproduce experiments in a general, qualitative sense. I am working to develop good data-driven models which can be generalized to any variety of physical systems, not just my particularly difficult system. Hopefully there will be a point in the future where I have access to good data, but that time is not now.

However, many researchers who have been attempting to create better high-fidelity models look down on this, claiming that there is no point trying to simulate a system if the inputs don't match nature exactly. "Garbage in, garbage out" is the standard derogatory epithet. This strikes me as odd, since they are the ones tasked with producing the accurate high-fidelity models, and they cannot do it. It also strikes me as anti-intellectual, spurning an avenue of research which will have measurable benefit once they make their models better.

Am I wrong in this sentiment? Is it stupid to pursue an area of research that is essentially dependent on future discoveries and improvements in modern methods? Are there any examples out there of research that was performed under the assumption that future methods would make it useful?
There are research papers that are discovered 20-30 years later and new interesting research is build on it, thanks to the internet and digital indexing nowadays. So if you can (?) publish your research and live for it and can finance it, nothing speaks against following your interest and intuition (e.g. Einstein)

I will take an approach to also work on very risky, unknown, niche questions the next years, apart from working on a funded research project fulfilling some TRL levels required. This gives me some freedom of research and I think I have to risk it to yield some possible high-impact papers. Most tenured researchers always work on several topics:


you have to risk something in research (money, time, ...)
it's diversification of such risks
you are paid to find original new stuff in comparison to industry research, not just climb up the TRL ladder (although several TRL steps are not covered by industry research and developement)


But working solely on a very risky topic with unclear future outcome or likelihood of publication is like betting all your money on one stock.

No phyiscal simulation software I know matches nature exactly. How could it?! Nature is nonlinear and in complex systems with many degrees of freedom any model is imperfect if you don't simulate the H-atom. If your methods/models are original and of significance is hard to answer here without knowledge of your exact research topic.

To take up Nates comment, mathematical topology knowledge is nowadays of tremendous importance in modern solid state physics, although mathematicians work probably since centuries on it. 

If your research is only "useful" in dependence of progress in other related research areas/methods (as you seem to think and qualify), than it doesn't look like fundamental research to me, because you wouldn't and don't have to think about usefulness. Rather applied research, and without any people interested in it or its results, this could be riding a dead horse. 

In conclusion I think you have to make a strategic decision and risk assessment, if you want to do research on fundamental aspects/questions or move closer to the research methods/data/questions of your peers. Putting them as wrong or having a finite horizon is probably a dead end for you, also most of fundamental research ideas end like this. Currently your methodological approach and questions seem too distant for them. But doing and producing research that can be published in fundamental research vs. applied research journals is a huge career decision to make, as different requirements for significance and originality exist.