I can assure you that anyone who touches numerical simulations of any kind (physicists, engineers, chemists, biophysicts...etc) has tried their hand at ML based surrogate models in the last 5 years, so it's not like they aren't being tested. From my experience, they aren't very robust. Weather modeling is actually one of the very few areas where it seems to work half decently.
Why does it work for weather at all? Is there something that the mathematical models are over-simulating? Is weather easier to predict than we thought? Just curious what the intuition is to regarding the success of ML weather modelling...
There is just A LOT of data available- usually an order of magnitude more than in any other related problem.
And general weather forecasts are not that hard - we have semi useful forecasts for more than 50 years. It’s when you want to do something special: long range, nowcasting of convective storm, other extreme weather etc. that is hard. And even then it’s as much a problem of input data accuracy than the models themselves.
Traditional physics based weather models also rely heavily on physical parameterization for sub grid scale processes (think clouds, microphysics of rain sleet snow, etc) so even the deterministic physics models are learned approximations from data.
traditional physics-based weather models also rely heavily on humans looking at the output and the evaluation of the output to discard wacky runs. Let's not pretend that existing physical models of the atmosphere stay on the rails all the time.
When you say runs that is an ensemble model forecast which is different than a deterministic model forecast. Any type of weather model sensitive to initial input state errors will have outlier model runs.
I suspect it's the massively rich, detailed, accurate, multimodal data.
For example, you have satellite images, coupled with real on the ground measurements of windspeed, sunlight, air pressure and precipitation going back decades.
No shit. If you want a tax cut to have an economic impact, cut taxes for the poorest, the ones with the least disposable income.
EDIT: Seems like the article is suggesting that giving more disposable income to the rich, results in them using that extra income to buy capital rent seeking assets. So it is a double fuck you to the poor. Because not only do the rich end up paying proportionally less in taxes, but they also use the extra money to compete with you for your home, buy the home and rent it back to you, putting you in an even worse financial situation.
Why are they scared shitless of wind power? What's the deal? I don't get it. Like, they seem to be more determined to stop wind power than solar power. Why?
Exactly what I was thinking. In Europe (e.g. UK or Germany) people often complain when chip shops or other fried food establishments open below them, because of the constant smells.
I don't remember when I first found out, but it blew my mind that sausages in the US are typically beef. Like, what? Sausages are like quintessentially pork products. Like the whole point is that you have a lot of off cuts of a cheap meat and you make sausages. Beef sausages? Yeah, that's like a step change in wealth, haha.
I don't know well "all other pork" and retail ground beef work as proxies for sausage meat. But ground beef roughly doubled in price from 2010 to the start of 2020. Before it did that, it was very close in price to "all other pork", and before 2000 a bit cheaper.
They are? That's not my personal experience, but maybe there's something unusual in the part of the US I live in. I mean, beef sausages exist and you can get them, but you generally have to choose them intentionally.
I wouldn't say they're "typically" all beef. When you go shopping for something like hotdogs for instance, if you just pick up a package of "hot dogs" or "weiners" they'll typically be a mixture of chicken, turkey, pork, and maybe beef. For instance:
Some people really look down on these kinds of products though and see this as a "mystery meat" of extremely poor quality cuts. The whole phrase of "knowing how the sausage is made" comes to mind. So that's then in comparison to "franks", which are often 100% beef and are often seen as higher quality:
Are they higher quality? Eh. They're probably about the same in the end. One big thing pushing up the popularity of beef franks over pork/chicken/turkey mix is an impression of Kosher hotdogs being of very high quality during the time when meat packing was really bad, so lots of even non-Jewish people will often see Kosher dogs as superior. Which, for obvious reasons, have beef and not pork.
All economic activity is a key driver to climate change. Why single out one thing? You're clearly imposing a self-serving moral judgement on other people.
I eat a lot of rice, but I don't come from any Asian tradition. I find parboiled to be the easiest to cook and is very versatile unless you need glutinous (sticky) properties, and often also the cheapest on the shelf (in non-Asian stores). With jasmine and basmati, although they are more fragrant, if you over cook them they'll end up bloated with water.
But these days I almost never cook rice in water but in a stock or sauce or stew or soup of some kind.
I don't have much to discuss about the movement (although I am sceptical due to the repeated historical pattern of Mensheviks making way for Bolsheviks), but the subject we are discussing are democratic socialist countries, not movements, which as far as I am aware, don't exist. There are social democracies like Finland, which is what I assume the poster was referring to.
You can use simulators. However the problem is that if you're for example running material science experiments, those simulations will consume a lot of compute and take weeks, so spamming different approaches in the way an agent tends to work might not work quite as well.
Work for a manufacturing company, and we spend a lot of time creating surrogate models for physical simulations. Huge speed ups, 100-1000x possible. You end up still using the “real” simulation for validation, but you can run orders of magnitude more simulations for early design and refinement first.
