Simulation, Consciousness, Existence
Hans Moravec, 1998.
https://frc.ri.cmu.edu/~hpm/project.archive/general.articles...
If you guess Fortran, you might be right:
(different ICON Project) "The infrastructure, ICON-Land, for this ICON-A land component has been newly designed in a Fortran2008 object-oriented, modular, and flexible way."
https://mpimet.mpg.de/fileadmin/publikationen/Reports/WEB_Bz...
Fortran alive and kicking:
Programming is something scientists tend to study only as far as needed to get results that look right. This is how the most influential COVID model ended up being a 15,000 line student-quality C program with hundreds of single-letter name global variables.
[1]: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2017MS00...
Eg how do hou predict the temperature at x,y? Is it ground type, water, sand?Altitude? Neighboring values thereof?
What are the inputs? Do you give it a starting point and apply it to a bunch of elements like some giant automata like game of life?
Some kind of finite element analysis thing?
So many questions.
The Earth system model provides a numerical laboratory for research on the climate dynamics on time scales of a season to millennia. Necessarily most processes are parameterized to allow the computationally efficient integration over long periods.
It's also mentioned it will contribute to DestinE[2]:
Destination Earth (DestinE) aims to develop – on a global scale - a highly accurate digital model of the Earth to monitor and predict the interaction between natural phenomena and human activities. [...] The initial focus will be on the effects of climate change and extreme weather events, their socio-economic impact and possible adaptation and mitigation strategies.
[1]: https://mpimet.mpg.de/en/science/models/icon-esm/
[2]: https://digital-strategy.ec.europa.eu/en/policies/destinatio...
https://resources.nvidia.com/en-us-fleet-command/watch-27?xs...
How do these two compare?
The modeling system in the linked article is a high-fidelity numerical simulation of the coupled Earth system. It's a giant PDE solver for Navier-Stokes applied to the Earth's atmospheres and oceans, coupled together with a great deal of additional physics simulation. The intent is to reproduce, in simulation, the Earth's atmospheric and oceans with the highest fidelity. This set of simulations is the culmination of nearly 70 years of investment, going back to the very first applications of digital computers for solving complex math equations (one of the first simulations bought for ENIAC was a crude quasi-geostrophic atmospheric mode / weather forecast).
NVIDIA's FourCastNet, while very cool, is quite literally a facsimile of this type of system. It's really not even in the same ballpark.
I want to dogfight over Ohio, land at Offutt to play Warzone in Omaha, then take a MRAP and drive to NY.
> Our ICON-ESM configuration is already used in production mode for scientific purpose with horizontal resolutions of 10 km, 5 km and 2.5 km. With the 1.2 km configuration we have now opened the door for a new class of numerical models which will allow us to investigate local impacts of climate change, such as extremes of precipitation, storms and droughts.
Some evidence of them using 10 km cells and then subdividing into halves, gets you down to 1.25 km.
Simulation of dynamic systems is a big deep area. In general you use what is called numerical simulation where you have a model describing your system, in the form of a partial differential equal equations.
You start with the chosen initial conditions, choose a delta-t as your time increment, and solve the equation for those inputs. That result is the input to the next iteration.
The most basic algorithm to solve such an equation is “Newton’s method” but no one actually uses that, they use many more advanced methods. But if you are learning that is where you start.
This approach has advanced greatly over the last 70 years. Doing numerical simulation is why early computing work got funding, to simulated nuclear reaction inside bombs.
Now numerical simulation is the occupation of all the worlds top super computers. It’s used for climate simulation, bridge strength, how sky scrapers flex in the wind, testing car crashes or even simulating the strength of ceramics. Oh and it used a lot in financial simulations to model risk and calculate the price of assets.
Its an interesting field. But its seems not so easy to get the real methods used by the bigger models.
(I'm not using a supercomputer...)
You initialize the system at some known state (I.e. set the temperature, pressure, etc. at all grid points to real world measurements) and then integrate a complex differential equation for the next time step and so forth. So it is not like a automaton. Finite elements analysis comes closer, but I think they use a different scheme like finite volume methods.
A lot of insight can be gained by [this](https://pure.mpg.de/rest/items/item_3379183/component/file_3...) paper. The first 10 pages should give you a rough overview.
Roughly speaking yes. Divide all into a grid of cells. Model a cell state with a bunch of numbers, apply some rules to update cell state with neighbors. The trick is to figure out rules of updating state. One needs to write differential equations first, incorporating all relevant physical processes into them, and to transform equations into those rules of updating, which will be a way more complex than with Game of Life.
Though it may be even more complex, like different time steps at different time-points, or changing a grid of cells to increase details in some areas where much is going on by slowing down simulation. Most of complications are due to a limited abilities of our computers: the idea to get more precision by calculating less.
The difficulty of making a "large" map comes from what you want to simulate and in how much detail, not from how big it is per se.
ED has an approximation of the entire milky way in an MMORPG, where you can visit individual planets of around 400 billion star systems. These are obviously "generated" except for maybe a few handcrafted systems like Sol.
The problem really isn't "size".
There's a LOT more I could ask about her, and that's just one character. This show is fan fiction LOOSELY based on the writings. VERY loosely.
That is all. I'll enjoy my ban now.
Surrogate models are nice because they can emulate the output of the full fidelity calculation in a fraction of the runtime, but they typically are trained within a range of validity outside of which they cannot reliably extrapolate.
But dynamically uninteresting, quasi-balanced setups and modes? There's far less to worry about in terms of the butterfly effect, and any errors you might worry about will be dwarfed by the fact that we don't have good data to assimilate in places like the remote oceans anyways.
It's also worth pointing out that the mathematics and understanding of error / perturbation growth in the atmosphere are well-understood. In fact, this fundamentally underpins how we've developed data assimilation approaches over the past two or three decades that allow us to effectively leverage new datasets such as satellite data to increase forecast quality and reliability at longer lead times. So it's somewhat trivial to actually directly quantify these "butterflies."
> It's not as though this is part of a growing trend to abandon conventional weather and climate modeling.
The thing is, there *absolutely is* a trend towards private investment in weather modeling going towards faux-moonshot ideas like cubesat constellations without demonstrated ROI and that would require evolutionary leaps forward in data assimilation, or for deep learning to replace weather models. A miniature version of this already played out with precipitation nowcasting - probably the easiest weather forecasting problem that you could approach with an AI system, yet the approaches that have been developed so far barely improve over optical flow or other simple approaches, let alone advance our capability to forecast, say, convective initiation.
The future of weather forecasting is larger ensembles (O(100-500) ensemble members, across 2-5 different models) of near-convective-resolving global models at meso-gamma (2-10 km resolution) fed into slightly more sophisticated statistical post-processing systems - almost certainly trained using simple AI/ML techniques on large-scale reforecasts of these parent model systems, or brute-forcing purely Bayesian statistical approaches.
There are some profound problems with that idea once you get below 10 meter or so, but I'll let you think that one through yourself.
This. No, not at all at current computer sizes, but at future computer sizes. This is the same mistake someone in the 1970's might make about billions having a smartphone today (supercomputer by their standards). Consider how everything at current computer sizes is effectively two dimensional, even stacked processors are still fundamentally 2D designs. There is still a lot of computing advancement ahead. 40 years from now they'll look back and think the same things we think when we look back 40 years, that the machines were so primitive, hardly anything could be done with them, and some will be nostalgic for them, talk about their strengths, while others will shake their heads and think even messing with the fastest workstation today is a waste of time. Just because we can't conceive of how, doesn't mean it's not possible, some day.
Thank you for telling me what a femtometer is, as though my using the word wasn't a pretty good indicator I knew what it was. You mean a femtometer is a real thing? And I just made that up out of thin air to mean a meter stick to give to women. What are the odds?
> There are about 2*140 atoms in the atmosphere. You can't even count to that number, let alone do any fluid dynamics to that.
Would you like me to explain how your argument is a straw man, or can I trust you to figure it out?
> I'm confident that we won't have femtometer scale simulations of the atmosphere before the sun becomes a red giant and swallows the earth.
Very colorful, but all you're really saying is that you are pessimistic about technology and about any staggeringly large advancements in computer design or weather sensor tech, while I, otoh, optimistically say I just don't know, but I bet computers will get faster, smaller and cheaper, and that within only a hundred years there will be weather tech that we are incapable of conceiving of today.
Sure, a femtometer is mind-bogglingly small, but it's only 15 orders of magnitude smaller than a meter. It's way bigger than a zeptometer. How is it even possible femtometers can be described so simply? But of course, there could never be any more advancements in mathematics, physics, computer engineering or our current understanding of weather and climate. We basically know all there is to know right now. Huh.
Due to sensitive dependence on initial conditons. Even using measurements at meter resolution will cause the accuracy of a forecast to begin to break down after only a few days.
> What even is "very precise" 100 day weather forecasting?
Anywhere from accurate to exact.
> I think it's very amusing to do the math on how much memory would be required to run a crude primitive equation dycore over even the tiniest of domains at femtometer resolution
And Bill Gates thought 64K should be enough for anybody. Do you really think computers will only have a few GB of memory 50 years from now?
> there absolutely is a trend towards private investment in weather modeling going towards faux-moonshot ideas like cubesat constellations without demonstrated ROI and that would require evolutionary leaps forward in data assimilation, or for deep learning to replace weather models
This straw man does not exactly demonstrate that conventional weather and climate modeling is being abandoned anytime soon. If the unconventional private investments aren't profitable, the market will deal with them.
> The future of weather forecasting is
much like the local weather, impossible to predict with any accuracy years into the future, and yet the tools used to measure it are consistently getting more accurate, cheaper and smaller. Maybe like bottle-openers, weather sensors may superfluously start appear on everything. The more widespread the measurements, the more data descibing initial conditions, the better the forecast will be at any interval.
* It's pretty hard to predict weather for 100 days, because you would also need to predict many other events in the future: forest fires, volcano eruptions, and many kinds of human activity that also affect weather. However great are your fluid dynamic models, and however well were they able to predict the future state from today's state, they wont help that.
At the moment, no. But maybe there will be a way to accurately sense weather data at any location from LEO. IR thermometers are pretty neat, maybe something metaphorically along those lines, a satellite with a laser technology that could beam back accurate weather data from any location, and all atmospheric locations it can see along its orbit, sending the data to ground-based ultra-computer networks running simulations.
In 1933, no one would have believed that GPS was 40 years away. In 1985 most would not have been able to understand how flat and thin color monitors were only a decade away, nor that mRNA vaccines were less that 30 years away. Similarly, we really don't know what the future of weather sensing and prediction will be like in 2070, and if we could know, we wouldn't understand how it would be possible.
That's an extremely simplistic take on things. In reality, one of the largest issues with high-resolution weather forecasts (1-3 km scale, convection-permitting simulations) is the fact that you small errors in the initialization or model dynamics lead to changes in small-scale storm structure that feedback onto larger scales of motions, disrupting the forecast. Ultra-fine measurements and simulation resolutions only exacerbate this tendency.
> Anywhere from accurate to exact.
You didn't answer the question. Are you trying to predict convective initiation at 100 days lead time? Are you trying to predict a particular synoptic system? Are you trying to predict whether or not it will be warmer than average or not? These are vastly different weather prediction problems which require different approaches.
> And Bill Gates thought 64K should be enough for anybody. Do you really think computers will only have a few GB of memory 50 years from now?
Modern weather and climate modeling is already a tera- or peta-scale endeavor, depending on exactly what one is trying to do. The sorts of simulations alluded to in the OP push into the exascale.
As other commenters have noted, your odd choice of femotometer (10^-15 meters) would lead to memory requirements larger than the number of atoms in the real atmosphere.
> This straw man does not exactly demonstrate that conventional weather and climate modeling is being abandoned anytime soon. If the unconventional private investments aren't profitable, the market will deal with them.
Of course it does. The age of heterogeneous compute for weather/climate models is just beginning, yet you do not see NVIDIA optimizing NWP systems to run on GPUs or Google porting them to run on TPUs, do you? Instead, you see these organizations pursuing AI/DL, while core NWP development is limited to federal research labs and agencies, but they are increasingly struggling to attract developer and research scientist talent to pursue these activities.
This is a very real challenge that is frequently talked about within the weather community in the United States. I'd hazard the guess that you are not a member of this community?
> much like the local weather, impossible to predict with any accuracy years into the future, and yet the tools used to measure it are consistently getting more accurate, cheaper and smaller. Maybe like bottle-openers, weather sensors may superfluously start appear on everything. The more widespread the measurements, the more data descibing initial conditions, the better the forecast will be at any interval.
There is virtually no data assimilation technology to support the ingestion of the vast majority these data, and we do not even run weather models with suitable configurations to take advantage of them if we had the DA support in the first place. And, as I've mentioned repeatedly, not every measurement leads to an improvement in forecast quality. This is simply _not_ the low- or even high-hanging fruit regarding improvements to weather forecast quality and impact.
I've worked in this exact domain of developing novel weather sensing and observation systems and leveraging them to try to improve forecast quality - across federally-funded research and more than one private company over the past ten years - and it's mostly a fools errand. If one wants to develop improved, impactful, useful weather forecasts, this is not the path to pursue.
https://en.wikipedia.org/wiki/Femtometre
I mean you can’t even fit a thermometer into a cubic femtometer..?
user@decadeoldcomputer:~$ echo 2^140 |bc
1393796574908163946345982392040522594123776
Maybe try to ignore who is saying what, and focus only on what was said.When you can't beat the argument, pull out the ad hominem fallacy and attack the man. Fallacy, of course, is faulty reasoning.
> So if you encode only one bit per cube of this fm cubic lattice, and you manage to encode this into single atoms of silicon, you need a volume of silicon 8,000,000,000,000,000 times larger than the system you model.
This explanation is indicative of linear thinking. Apparently Google Earth is not possible, as it would require a computer the size of the planet. Digitizing the Library of Congress apparently requires a memory stick the size of Congress. Seriously? You just can not comprehend how things could ever get better than your current understanding of how things are right now? Consider that if you lived in 1500BC, were an expert at the time in farming, and a plough was described to you, you would mock the person describing it, and insist that tilling soil was impossible.
And your second paragraph is amply demonstrating this. I pointed out the physical implications of encoding your femtometer cubes at atomic scale. Nothing more. Encoding the Library of Congress has nothing to do with that. You are proposing to simulate at subatomic scale so obviously encoding it into atoms will make the simulation larger than the object similated.
To engage with your argument directly: You have none. All you repeat is that the past has seen technological breakthroughs, therefore the specific fantasy you propose makes sense. Non sequitur. That some breakthroughs have happened doesn't mean that any random breakthrough will happen. And your ideas are pushing hard against the limits of physics.
Incorrect. Any statement concerning the arguer and ignoring their argument is ad hominem and fallacious argument.
> And your second paragraph is amply demonstrating this.
Incorrect and a tu quoque argument. I did not address you, personally, but only your argument, and if my use of "you" is confusing, it is the royal you, "you all," and I may as well have used "we."
> I pointed out the physical implications of encoding your femtometer cubes at atomic scale.
Using current understanding of how it would have to be done and in denial that it might ever possibly be done more efficiently in the future.
> Nothing more.
>>> I think Maursault has thoroughly demonstrated their lack of serious thought
other than this ad hominem.
> You are proposing to simulate at subatomic scale so obviously encoding it into atoms will make the simulation larger than the object similated.
and this straw man
> To engage with your argument directly: You have none.
but wait while you prove yourself wrong,
> All you repeat is that the past has seen technological breakthroughs,
Yes, my argument is that technology advances, and since it has always done so, my crazy idea is that it will keep on doing so. Though it is possible technology has stopped advancing, I think it is unlikely.
> therefore the specific fantasy you propose makes sense. Non sequitur.
The specific fantasy is your straw man, and I drew no conclusions, those are yours. Frankly, my first comment was a joke and not meant to be taken literally, and I only intended to argue against someone else's idea that increasing simulation resolution is not the future of weather modeling, and you're not the only one that got tripped up by my use of femtometer. Regardless, I still see it as possible territory, and anyone else not being able to conceive of how does not mean it is impossible, only that we can't conceive it, just like Bill Gates, not a stupid man, was unable to conceive of anyone needing more than 64KB of RAM, so you also are unable to conceive of how something that would require incredible, inconceivable advances in technology to achieve, and yet, within the next 5 years modern medicine will advance further than it has in all the years before, that's just how it is, and students of the history of technology know this the same way you know how many femtometers there are in silicon's lattice spacing. What you seem to be unable to do is understand there are things we don't understand, and in physics, a good example of this is dark matter, which for some reason we haven't figured out a way to detect it, and it is so similar to the luminiferous ether in this regard, that only students of the history of science suspect that it might be bullshit, while every scientific mind is convinced it exists, just like the luminiferous ether in 1886.
> That some breakthroughs have happened doesn't mean that any random breakthrough will happen. And your ideas are pushing hard against the limits of physics.
As far as you or anyone else knows, today. But we don't know the future, and we never have. This is not my proof that this will occur, only that we don't know.
1 is a number.
2 are two numbers (1 + 1).
3 are three numbers (1 + 1 + 1).
------------------------------>
∴2^140 are 1393796574908163946345982392040522594123776 numbers.
quod erat demonstrandumRelatedly, last night I tried to think about sqrt(2), but I couldn't get up that high! I got stuck trying to remember the 43'd irrational number after zero.