Comment by tictacttoe | Hacker News Reader

Comment by tictacttoe | Hacker News Reader

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As a PhD candidate in high energy nuclear theory, I've observed a lot of 'math wacking', i.e. reading theory papers which include pages and pages of detailed equations without a single experimental data point. Just take a look at arXiv/nucl-th if you want to see what I'm talking about. These same guys go to conferences, poo-poo less first-principle calculations which at least attempt to describe experiment, and espouse their untested pet theories as if it were gospel.

To be honest, I've been extremely disappointed by the scientific process in high-energy particle physics. People are so tethered to their theory convictions, that they accept derivations as physical realities and dismiss the importance of experimental validation. The old farts in the field are also completely averse to computational/numerical methods and dismiss the importance of production quality code development.

So while I embrace the sentiment that 'math is the essential language of physics', if you can't embed the mathematics into a simulation which describes reality, then what are you doing as a scientist?

I agree that they hate production quality code and numerical methods, but in some of these DFT codes for example (HF is only somewhat better), you fix some numerical bug and a bunch of theoretical results agreeing with experiment go straight out the window and with it your confidence in the computational model. These 'old farts' have probably seen this happen way too many times and on top of it from people who lack the basic theory knowledge, let along experimental, to point out a crap result from the code.

The space of possible experiments is enormous. If someone takes time to find a conclusion that the theory predicts from the first principles but for which experimental data are lacking, may it be somehow valuable? Especially if the theoretically predicted effect is somehow interesting.

The goal is to disprove theory's not to validate them. Remember any valid counter example can disprove a trillion pieces of supporting evidence.

http://www.nytimes.com/interactive/2015/07/03/upshot/a-quick...

You can always over fit for existing data. Useful theory's should suggest a novel experiments to test them.

The experimental data may prove or disprove a theory. But when it's lacking, there's no way to tell. So finding areas where experiments were not made, but can be made, and where the theory predicts some clear results, can serve as a means of falsification.

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