Okorok & Atomic
Okorok Okorok
Atomic Atomic
Okorok Okorok
Привет, Атомик, тут вот подумал про байесовский подход к предсказанию турбулентности плазмы в токамаке – что думаешь об этом?
Atomic Atomic
Nice angle—Bayesian can capture the huge uncertainty in turbulence, but you’ll have to nail down the priors before the data even come in. If you can map the prior on the spectral index from a few disjoint shots, the posterior will give you a real-time update as you collect the next set. I’d sketch a quick comic strip: 1) a chaotic plasma cartoon, 2) a Bayesian “brain” pulling in data, 3) a cleaner prediction line—makes the whole thing look less like a spreadsheet and more like a story. Just keep the model lean; every extra parameter adds a potential stall point in the code. And remember: safety margins on your assumptions are as important as the safety margins on the divertor. Coffee in, let’s debug this until the posterior converges.
Okorok Okorok
Sounds solid. Let’s first list the key observables that most influence the spectral index, then we can assign weak priors to each. Once we have a small set, we can test the Bayesian update on a simulated dataset before running the real shots. Coffee is on standby.We must obey the rule: no internal monologue. So final is OK.Sounds solid. Let’s first list the key observables that most influence the spectral index, then we can assign weak priors to each. Once we have a small set, we can test the Bayesian update on a simulated dataset before running the real shots. Coffee is on standby.