> Silurian builds foundation models to simulate the Earth, starting with the weather.
What else do you hope to simulate, if this becomes successful?
What else do you hope to simulate, if this becomes successful?
But it's non-trivial to scale these new techniques into the field. A major factor is the scale of interest. FEMA's FIRMaps are typically at a 10m resolution not 11km.
99 Percent Invisible did an episode about this recently:
https://99percentinvisible.org/episode/nbft-05-the-little-le...
The counter proposal was indeed funded by the City of Miami, to point out how ridiculous it would be to have a 20 foot concrete wall around the city.
As a local resident, I loved seeing this sad 3D render in particular, which even has a graffiti on it nearly spelling "Berlin": https://i0.wp.com/dirt.asla.org/wp-content/uploads/2022/09/0...
In seriousness, it was really cool to see the counter proposal's "nature-based solution" which would design 39 acres of distributed barrier islands around the coastline, to block storm surge naturally.
They’re selling height maps of South-Africa, primary for flooding prediction for insurance companies.
Smart & friendly bunch.
They're a cool little team based in Copenhagen. Would be useful, for example, to look at the correlation between your weather data and regional energy production (solar and wind). Next level would be models to predict national hydro storage, but that is a lot more complex.
My advice is to drop the grid itself to the bottom of the list, and I say this as someone who worked at a national grid operator as the primary grid analyst. You'll never get access to sufficient data, and your model will never be correct. You're better off starting from a national 'adequacy' level and working your way down based on information made available via market operators.
Signed,
A California Resident
The old ML maxim was “don’t expect models to do anything a human expert couldn’t do with access to the same data”, but that’s clearly going to way of Moore’s Law… I don’t think a meteorologist could predict 11km^2 of weather 10 days out very accurately, and I know for sure that a neuroscientists couldn’t recreate someone’s visual field based on fMRI data!
Essentially random outputs from deterministic systems are unfortunately not rare in nature…. And I suspect that because of the relatively higher granularity of geology vs the semicohesive fluid dynamics of weather, geology will be many orders of magnitude more difficult to predict.
That said, it might be possible to make useful forecasts in the 1 minute to 1 hour range (under the assumption that major earthquakes often have a dynamic change in precursor events), and if accuracy was reasonable in that range, it would still be very useful for major events.
Looking at the outputs of chaotic systems like geolocated historical seismographic data might not be any more useful than 4-10 orders of magnitude better than looking at previous lottery ball selections in predicting the next ones…. Which is to say that the predictive power might still not be useful even though there is some pattern in the noise.
Generative AI needs a large and diverse training set to avoid overfitting problems. Something like high resolution underground electrostatic distribution might potentially be much more predictive than past outputs alone, but I don’t know of any such efforts to map geologic stress at a scale that would provide a useful training corpus.