Speaker 1: Welcome to Bedtime Astronomy. Explore the wonders of the cosmos
Speaker 1: with our soothing Bedtime Astronomie podcast. Each episode offers a
Speaker 1: gentle journey through the stars, planets, and beyond, perfect for
Speaker 1: unwinding after a long day. Let's travel through the mysteries
Speaker 1: of the universe as you drift off into a peaceful
Speaker 1: slumber under the night sky.
Speaker 2: Today we are undertaking a simulation of well, what was
Speaker 2: long thought to be the impossible. When you look up
Speaker 2: at the night sky, what you're really staring into is
Speaker 2: a massive, almost incomprehensible computation problem. We live in the
Speaker 2: Milky Way Galaxy. It's home to over one hundred billion
Speaker 2: individual stars, and every single one of those stars, plus
Speaker 2: all the gas and dust and dark matter swirling around,
Speaker 2: is governed by a complex web of physics. For decades,
Speaker 2: the ultimate challenge for astrophysicists, kind of their Mount Everest,
Speaker 2: has been to create an accurate star by star model
Speaker 2: of this entire system to trace its history, its interactions,
Speaker 2: and its future. It's just it's always been out of reach,
Speaker 2: but the game has fundamentally changed. Our deep dive today
Speaker 2: is focused on a revolutionary breakthrough from a team of
Speaker 2: international researchers led by kiya Hiroshima at Reichen in Japan.
Speaker 2: Their work was just published, and it describes as simulation
Speaker 2: method so fast and so detailed that it has completely
Speaker 2: shattered the computational bottlenecks that have held back galactic modeling
Speaker 2: for years.
Speaker 3: This is genuinely a step change moment. I mean, it's
Speaker 3: not just an improvement. It's a completely different league. And
Speaker 3: you really need to hear the numbers to appreciate just
Speaker 3: how big this achievement is.
Speaker 2: Okay, laid them on us well.
Speaker 3: Previous state of the art simulations, the best we had,
Speaker 3: they struggled to get anywhere close to the true fidelity
Speaker 3: of our galaxy. This new model, it successfully and accurately
Speaker 3: represents more than one hundred billion individual stars. That right
Speaker 3: there is the resolution breakthrough.
Speaker 2: One hundred billion, so true star by.
Speaker 3: Star, true star by star. But the truly staggering part,
Speaker 3: the part that really changes things, is the efficiency game.
Speaker 3: They did this at a rate more than one hundred
Speaker 3: times faster than was possible before.
Speaker 2: Wait say that again, one hundred times the detail and
Speaker 2: one hundred times the.
Speaker 3: Speed at the same time. Thing about what that means
Speaker 3: We aren't talking about a small improvement. We're talking about
Speaker 3: going from a theoretical experiment that would take decades to
Speaker 3: something you can actually run in a matter of months.
Speaker 3: This is, and it's no exaggeration, the world's first simulation
Speaker 3: to tackle the Milky Way with true starbuy star fidelity.
Speaker 2: Okay, we have to unpack this because a one hundredfold
Speaker 2: jump in both speed and resolution, it sounds less like
Speaker 2: engineering and more like some kind of magic trick. We
Speaker 2: need to understand what the roadblock was exactly, and then
Speaker 2: how this team managed to combine artificial intelligence with traditional
Speaker 2: simulations to just bypass it completely.
Speaker 3: Absolutely, and you're right. To really appreciate the solution, we
Speaker 3: have to get deep into the problem because the limitation
Speaker 3: wasn't just a lack of computing power. It was a
Speaker 3: fundament old conflict baked into the physics itself.
Speaker 2: Let's start right there. Then, let's define this astronomical hurdle.
Speaker 2: Why was simulating the Milky Way star by star with
Speaker 2: just conventional methods considered practically impossible for so long?
Speaker 3: Well, the whole point of these efforts is pure scientific discovery, right,
Speaker 3: It's about understanding how galaxies evolve. Astrophysicists build these incredibly
Speaker 3: complex models of galaxy formation, structure, and how stars evolve
Speaker 3: so they can test their biggest theories.
Speaker 2: So they build a digital universe to see if it
Speaker 2: matches the real one exactly.
Speaker 3: They need a model that's detailed enough to compare its output,
Speaker 3: say the distribution of heavy elements, against what we actually
Speaker 3: see with telescopes like the Hubble or James Web. If
Speaker 3: the model doesn't match reality, then the theory is wrong
Speaker 3: or at least incomplete.
Speaker 2: And when you say an accurate model of a galaxy,
Speaker 2: we're not just talking about plotting points on a map.
Speaker 2: You're talking about modeling the entire engine of the system,
Speaker 2: all the physics.
Speaker 3: That's it is the definition of a multiphysics problem on
Speaker 3: a gargantuan scale. To get it right, the model has
Speaker 3: to account for multiple wildly complex interacting phenomena all at
Speaker 3: the same time, such as you've got gravity, the universal
Speaker 3: influence of it. Then you have the fluid dynamics of
Speaker 3: these huge turbulent clouds of gas and dust. You have
Speaker 3: catastrophic supernova explosions releasing immense energy, and on top of
Speaker 3: all that, the continuous process of creating new elements inside
Speaker 3: every single star.
Speaker 2: It sounds like trying to run a trillion different interconnected
Speaker 2: experiments all at once.
Speaker 3: That's a really good way to put it. And the
Speaker 3: hardest part of it all is what scientists call the
Speaker 3: scale disparity.
Speaker 2: Scale disparity.
Speaker 3: All these things are happening on vastly different scales of
Speaker 3: space and time. Think about it, gravity, which dictates the
Speaker 3: overall shape of the galaxy, the spiral arms. That's a huge, slow,
Speaker 3: large scale thing. It plays out over billions of years
Speaker 3: and thousands of light years.
Speaker 2: Okay, the big picture, right.
Speaker 3: But then you have a supernova that's a small, fast,
Speaker 3: local event. It plays out in a tiny region of
Speaker 3: the galaxy over maybe tens of thousands of years or
Speaker 3: even less a blink of an eye in cosmic time.
Speaker 3: And trying to cram those vastly different scales into one
Speaker 3: single cohesive model, well, that's what creates a paralyzing computational conflict.
Speaker 2: Let me see if I can wrap my head around
Speaker 2: this with an analogy. Let's say you want to make
Speaker 2: a video of a flower growing from a seed all
Speaker 2: the way to a full bloom that takes months.
Speaker 3: Okay, yeah, but while.
Speaker 2: You're filming that flower, a tiny hummingbird flashes past the
Speaker 2: frame in one hundredth of a second. If your goal
Speaker 2: is to capture both the slow growth of the flower
Speaker 2: and the precise rapid wing beats of the hummingbird with
Speaker 2: equal detail, the speed of your camera, your frames per
Speaker 2: second has to be dictated by the fastest event, by
Speaker 2: the hummingbird.
Speaker 3: That is precisely the problem that is the challenge for
Speaker 3: the supercomputer. The flower is the galaxy structure, slow and
Speaker 3: governed by gravity. The hummingbird is the supernova that localized
Speaker 3: violent explosion. If the model has to track what's happening
Speaker 3: locally at the speed of that explosion, it has to
Speaker 3: use impossibly short timesteps, little tiny snap shots in time.
Speaker 2: And doing that for the whole system is the killer.
Speaker 3: That's what killed it. Demanding those ultra short time steps
Speaker 3: across a system of one hundred billion stars is what
Speaker 3: made simulating the entire galaxy.
Speaker 2: Impossible, which leads us right to the limitations of the
Speaker 2: previous state of the art. If you couldn't run the
Speaker 2: whole galaxy with those short timesteps, you had to compromise
Speaker 2: on the detail, on the resolution. So what was the
Speaker 2: compromise the.
Speaker 3: Source material is very clear on this previous high resolution attempts,
Speaker 3: they had an upper mass limit of only about one
Speaker 3: billion sons.
Speaker 2: One billion when the Milky Way has over one hundred
Speaker 2: billion exactly.
Speaker 3: So they were forced into this will disastrous compromise. They
Speaker 3: had to sacrifice individual star resolution. They use what the
Speaker 3: researchers called the star cluster particle. The smallest unit in
Speaker 3: the model wasn't a single star. It was a cluster
Speaker 3: of stars, often weighing one hundred sons or more, all
Speaker 3: treated as a single particle.
Speaker 2: Wait a minute, If the whole point is starby star fidelity,
Speaker 2: how can you justify lumping one hundred sons together and
Speaker 2: calling it one thing. Doesn't that just defeat the purpose
Speaker 2: from the start.
Speaker 3: It does, and that's exactly why the fidelity was so low.
Speaker 3: Will you average out one hundred separate stars at one
Speaker 3: data point? You lose the ability to model the detailed
Speaker 3: physics of how stars actually evolve. What happens to the
Speaker 3: individual massive stars, the ones that live fast, die young,
Speaker 3: and go supernova that just gets completely lost. It's averaged
Speaker 3: out by the group.
Speaker 2: And soupernovad are critical, aren't they They're the element factories.
Speaker 3: They're everything. They are the primary way heavy elements get
Speaker 3: created and distributed. They're the raw ingredients for new stars,
Speaker 3: for planets, and ultimately for life. So if you're averaging
Speaker 3: out the physics of that explosion, because your smallest particle
Speaker 3: represents one hundred suns, you miss the crucial feedback loops.
Speaker 3: You might see the general shape of a spiral arm shore,
Speaker 3: but the precise local dynamics, the energy injection, the shockwaves,
Speaker 3: all of that is just lost to the averaging.
Speaker 2: So you get the big picture, the slow rotation of
Speaker 2: the galaxy, but you completely miss the details of how
Speaker 2: stars are born or how the space between stars gets
Speaker 2: enriched with new elements. It's a macromodel that can't answer
Speaker 2: the micro questions.
Speaker 3: It's exactly right, and the reason you can't have both
Speaker 3: high resolution and the full galaxy. It all circles back
Speaker 3: to that time penalty. If you try to use short
Speaker 3: time steps to see those fast local changes at the
Speaker 3: star level, you have to recalculate the physics of one
Speaker 3: hundred billion stars and all the gas way way more often,
Speaker 3: the computational cost just grows. Well, it's faster than exponentially
Speaker 3: as you crank up the resolution. High resolution demands short
Speaker 3: time steps, which exponentially increases the work, pushing the total
Speaker 3: run time beyond anything feasible. They were trapped, completely trapped
Speaker 3: in a classic computational trade off. You could have high
Speaker 3: resolution over a tiny piece of the galaxy or low
Speaker 3: resolution over the whole thing, but you absolutely could not
Speaker 3: have both at the same time.
Speaker 2: So they needed a way to satisfy the demands of
Speaker 2: the big slow gravity part of the simulation while still
Speaker 2: getting the results of the small fast supernova part, but
Speaker 2: without actually having to calculate the supernova physics from scratch.
Speaker 2: For every single star that explodes.
Speaker 3: And that leads us directly to the cold hard numbers,
Speaker 3: to the computational wall they hit. Let's quantify what this
Speaker 3: trade off really meant in terms of real world time.
Speaker 2: This is where the scale of the challenge just becomes prohibitive.
Speaker 2: Right If a team wanted to model the Milky Way
Speaker 2: star by star using the best conventional methods before this breakthrough,
Speaker 2: what was the actual cost in computer time?
Speaker 3: The barrier was well astronomical. Literally, if you took the
Speaker 3: best traditional physical simulation and tried to run it with
Speaker 3: the high resolution needed for individual stars. Yeah, it would
Speaker 3: require three hundred and fifteen hours of compute time, three
Speaker 3: hundred and fifteen hours for every one million years of
Speaker 3: simulated galactic history.
Speaker 2: Okay, three hundred and fifteen hours for a million years.
Speaker 2: That seems huge, but it's hard to grasp until you
Speaker 2: put it in the right context. We're not simulating just
Speaker 2: a million years. How long do astrophysicists need to model
Speaker 2: to see anything meaningful happen?
Speaker 3: To see the big structural changes things like spiral arms
Speaker 3: forma or stars migrating, or the long term impact of
Speaker 3: all that supernova feedback, researchers really need to simulate at
Speaker 3: least a billion years of evolution. A billion Okay, So
Speaker 3: now let's scale up those three hundred and fifteen hours.
Speaker 3: A billion is one thousand millions, so one thousand times
Speaker 3: three hundred and fifteen hours. That's simulating that crucial one
Speaker 3: billion year timeframe. Using the old methods would take more
Speaker 3: than thirty six years of continuous, dedicated real world compute time.
Speaker 2: Thirty six years, thirty six years. That's an entire career.
Speaker 2: That's a fundamental barrier to discovery. No research grant, no
Speaker 2: PhD program, no single research group, can commit to running
Speaker 2: one test for three and a half decades.
Speaker 3: The hardware would be obsolete three times over before the
Speaker 3: simulation even finished running.
Speaker 2: It wasn't just difficult, then, it was a hard stop.
Speaker 3: It was a hard stop on certain lines of scientific inquiry.
Speaker 3: If you have a brilliant new theory about, say, how
Speaker 3: star clusters form, and testing it requires a billion year
Speaker 3: high resolution simulation, You're just blocked. You can't do it. Yeah,
Speaker 3: you're forced to use those low resolution star cluster particles,
Speaker 3: which severely limits the kinds of questions you can even
Speaker 3: ask in the first place.
Speaker 2: So the obvious and maybe crude answer that usually works
Speaker 2: in computing is just throwing even bigger supercomputer at it.
Speaker 2: Build a bigger machine, throw ten times the cores of
Speaker 2: the problem. And wait, why wasn't that a viable solution here?
Speaker 3: Because of the principle of diminishing returns and specifically a
Speaker 3: crippling problem called communication overhead. Okay, building a bigger supercomputer
Speaker 3: solve the raw processing power issue, sure, but it doesn't
Speaker 3: solve the efficiency problem of keeping all those processors in sync.
Speaker 3: Think about how these things work. You take your one
Speaker 3: hundred billion stars and all the gas and you distribute
Speaker 3: the calculations across millions of process or cores or nodes.
Speaker 3: But here's the catch. Every single core needs to know
Speaker 3: what every other core is doing at every single timestep.
Speaker 2: There's a constant chatter between them.
Speaker 3: A constant massive flow of data to synchronize the physical
Speaker 3: state of the galaxy. So even if each individual core
Speaker 3: is calculating its little piece of a puzzle incredibly quickly,
Speaker 3: the system as a whole has to wait for everyone
Speaker 3: to catch up and share their results.
Speaker 2: So the whole system moves at the speed of the
Speaker 2: slowest link in the chain, precisely.
Speaker 3: And as you add more and more cores, the amount
Speaker 3: of time the system spends just communicating between them, managing
Speaker 3: that data flow, synchronizing the calculations, it starts to outweigh
Speaker 3: the time spent actually doing the physics. The system gets
Speaker 3: bogged down in its own overhead.
Speaker 2: It's like having a meeting with a million people. You'd
Speaker 2: spend all your time just trying to get everyone on
Speaker 2: the same page, and no time making decisions.
Speaker 3: That's a perfect analogy. The synchronization costs is the bottleneck.
Speaker 3: Adding more people or more cores just increases the noise
Speaker 3: and the waiting time, not the actual speed of the work.
Speaker 3: So simply scaling up the hardware was not a path
Speaker 3: out of that thirty six year weight. They needed a
Speaker 3: paradigm shift, not just a hardware upgrade.
Speaker 2: The conventional equations, no matter how fast you ran them,
Speaker 2: demanded that you calculate every tiny, dear detail of the
Speaker 2: fastest event across the whole system, at every single snapshot.
Speaker 2: That was the core inefficiency correct.
Speaker 3: The computational wall wasn't just a lack of transistors, It
Speaker 3: was a fundamental efficiency problem in the method itself, trying
Speaker 3: to model these vastly different time scales simultaneously with rigid
Speaker 3: traditional physics equations.
Speaker 2: And this brings us to the game changing solution, the
Speaker 2: AI shortcut. This is where the innovation team comes in
Speaker 2: and essentially tells the computer, you don't need to calculate
Speaker 2: this from scratch. Every time we've taught an AI, the
Speaker 2: answer tell us about the core methodology they used.
Speaker 3: So the team led by Hiroshima at Reichen's Ethem Center,
Speaker 3: along with collaborators from the University of Tokyo and University
Speaker 3: tak Day Barcelona, they focused on one thing, breaking the
Speaker 3: dependency between the fast small scale physics and the slow
Speaker 3: large scale physics. Their core idea is to combine the
Speaker 3: high fidelity traditional physical simulations for the large scale with
Speaker 3: a specialized AI technique known as the deep learning surrogate
Speaker 3: model for the small scale.
Speaker 2: Okay, the term surrogate model is key here. It's not
Speaker 2: replacing the entire physics simulation, right, It's replacing just one component,
Speaker 2: the most expensive component exactly.
Speaker 3: And which component do you think they targeted?
Speaker 2: The hummingbird? The supernova.
Speaker 3: The supernova, as we discussed, that's the primary bottleneck. It's rapid,
Speaker 3: it's localized, it has incredibly complex fluidynamics, and it demands
Speaker 3: those tiny timesteps that paralyze the main simulation. When a
Speaker 3: massive star dies, the gas around it is shocked, heated,
Speaker 3: and expands rapidly. Calculating that complex feedback with conventional methods
Speaker 3: is what was costing three hundred and fifteen hours for
Speaker 3: every million years of simulated time.
Speaker 2: So how do you train an AI to solve a
Speaker 2: problem like that, a fluid dynamics problem.
Speaker 3: Well, you leverage existing high resolution simulations. Before this breakthrough,
Speaker 3: researchers could run incredibly detailed, short simulations of a single
Speaker 3: supernova explosion just in a small isolated box, not inside
Speaker 3: a whole hundred billion star galaxy.
Speaker 2: Right. They could do the micro just not the micro
Speaker 2: and macro together, right.
Speaker 3: So the team took terabides of data from these ultra
Speaker 3: high resolution standalone supernova simulations and fit it all to
Speaker 3: a jeep learning model. The AI wasn't trained to solve
Speaker 3: the differential equations of fluid dynamics itself. It was trained
Speaker 3: to learn the input output relationship. Basically, given these starting
Speaker 3: conditions gas density, stellar mass, et cetera, what will the
Speaker 3: precise state of the surrounding gas be ten thousand years,
Speaker 3: fifty thousand years, and one hundred thousand years after the explosion.
Speaker 2: So it's pattern recognition, but for physical outcomes. The AI
Speaker 2: learned to predict the result of a month of calculation
Speaker 2: in just a few milliseconds.
Speaker 3: That's it exactly. That is its predictive power. The deep
Speaker 3: learning model learned to accurately predict how the gas expands
Speaker 3: and how energy is injected back into the galaxy over
Speaker 3: that critical one hundred thousand year period after a supernova.
Speaker 3: This allowed the main simulation to bypass millions of repetitive,
Speaker 3: tiny fluid dynamics calculations every single time a star died.
Speaker 2: That makes the computational advantage crystal clear. Instead of the
Speaker 2: main supercomputer grinding to a halt to calculate a shockwave
Speaker 2: for one hundred thousand years the old way, it just
Speaker 2: it flags the event. The AI surrogate model spits out
Speaker 2: the accurate high resolution outcome in a fraction of a.
Speaker 3: Second, and the main simulation just inserts that result and
Speaker 3: keeps on chugging along with its large scale calculations. Brilliant
Speaker 3: it is the main simulation can now take these big,
Speaker 3: comfortable time steps based on the slow pace of galactic gravity.
Speaker 3: It would only have to momentarily check in with the
Speaker 3: AI to inject the necessary fine scale physics the supernova
Speaker 3: feedback as needed. This ability to delegate the toughest computational
Speaker 3: lift is what unlocked the one hundredfold speed boost. They
Speaker 3: kept the high resolution detail because the AI is providing that,
Speaker 3: but they completely avoided the time penalty.
Speaker 2: The strategic delegation keeping the traditional physics for the macro
Speaker 2: structure and using AI for the microphysics. That feels like
Speaker 2: a fundamental re architecture of how we approach computational science.
Speaker 3: It is, But of course the very next question has
Speaker 3: to be how can you trust it? How can you
Speaker 3: trust a result provided by an AI shortcut? Right?
Speaker 2: If the model is predicting outcomes instead of calculating them
Speaker 2: from first principles, how do you know the physics is
Speaker 2: still valid?
Speaker 3: Trust is everything here, and the team knew that they
Speaker 3: put the model through a rigorous verification process. They didn't
Speaker 3: just accept the AI's output as gospel. They ran their
Speaker 3: new AI accelerated simulation and then compared its results against
Speaker 3: large scale tests using some of the world's most powerful
Speaker 3: conventional supercomputers.
Speaker 2: So they ran the old slow method on a smaller
Speaker 2: scale to check the AI's work exactly.
Speaker 3: They used Reichen's own Fugaku supercomputer, one of the fastest
Speaker 3: machines on the planet, and the University of Tokyo's Miami
Speaker 3: supercomputer system. They'd run test simulations of smaller galaxies or
Speaker 3: specific regions using the old full physics calculations, and the
Speaker 3: comparison showed that the AI surrogate model was indeed accurately
Speaker 3: representing the physics it was designed to emulate. That verification
Speaker 3: step was critical. It validated the AI as a genuine
Speaker 3: physics tool, not just a data analysis engine. It proved
Speaker 3: the shortcut maintained scientific fidelity.
Speaker 2: Okay, so after validating the physics we get to the payoff.
Speaker 2: Let's talk about the breakthrough results and what this means
Speaker 2: for the science of the cosmos and maybe for things
Speaker 2: beyond that.
Speaker 3: We can finally put that transformative speed increase into concrete terms.
Speaker 3: Remember the old timeline three hundred and fifteen hours of
Speaker 3: compute time just to simulate one million years of galactic evolution.
Speaker 2: The number that was the single biggest inhibitor to this
Speaker 2: kind of research.
Speaker 3: That time requirement dropped from three hundred and fifteen hours
Speaker 3: to only two point seven eight hours.
Speaker 2: Wow, two point seven eight that is just an unbelievable acceleration.
Speaker 2: You're trading nearly two weeks of waiting for a single
Speaker 2: data point for less than three hours. That changes the
Speaker 2: entire flow of how you do research.
Speaker 3: And now let's scale that up to the big prize
Speaker 3: that one billion years of galaxy evolution. The waytime collapses
Speaker 3: from thirty six years to something actually.
Speaker 2: Manageable, don't leave us in suspense.
Speaker 3: The thirty six year simulation time would, which was an
Speaker 3: impossibility for any research group, can now be accomplished in
Speaker 3: a mere one hundred and fifteen days.
Speaker 2: One hundred and fifteen days less than four months. That is,
Speaker 2: that's truly incredible. A researcher can now propose a fundamental
Speaker 2: question about how galaxies form, start the simulation, when their
Speaker 2: grant gets approved, and how the complete results before the
Speaker 2: academic year is even over.
Speaker 3: It completely transforms theoretical astrophysics from this multigenerational pursuit into
Speaker 3: a rapid response science. It absolutely does. The scientific impact
Speaker 3: is finally the ability to achieve individual star resolution across
Speaker 3: an entire large realistic galaxy with over one hundred billion stars. Previously,
Speaker 3: we had models of galactic structure, or we had models
Speaker 3: of stellar evolution, but never a single comprehensive model that
Speaker 3: linked the two in real time.
Speaker 2: So now you can see the whole system working together.
Speaker 3: Now researchers can run these simulations where they can literally
Speaker 3: trace the path of individual stars, watch them explode, and
Speaker 3: then observe precisely how the energy and elements from that
Speaker 3: explosion interact with the surrounding gas clouds, influencing the birth
Speaker 3: of the next generation of stars. Yeah, this level of detail,
Speaker 3: combined with this speed was just completely unreachable before.
Speaker 2: And as the source material pointed out, this isn't just
Speaker 2: for academic curiosity about distant physics. This detailed tracking brings
Speaker 2: us right back to our own existence, doesn't it.
Speaker 3: Precisely, the ultimate goal here is understanding our own origins.
Speaker 3: Achieving this star by star fidelity lets researchers trace how
Speaker 3: the heavy elements that make up rocky planets and eventually
Speaker 3: elements like carbon, oxygen, iron, were forged in the hearts
Speaker 3: of massive.
Speaker 2: Stars, distributed by these fast, violent supernova explosions, and.
Speaker 3: Then seeded throughout the galaxy over billions of years. We
Speaker 3: are now capable of running a detailed history of the
Speaker 3: chemical evolution of our home.
Speaker 2: But what is arguably the most profound implication of this
Speaker 2: whole breakthrough is that it extends far beyond the stars.
Speaker 2: The team solved a foundational problem how to link small
Speaker 2: fast processes to large slow processes, and that computational challenge
Speaker 2: is not unique to astrophysics.
Speaker 3: That is the crucial broader takeaway. This AI accelerated approach
Speaker 3: is a blueprint. It's designed to transform all multi scale simulations,
Speaker 3: any problem that requires linking microscale and macroscale phenomena across
Speaker 3: vastly different timeframes, and.
Speaker 2: The researchers themselves pointed to other fields they did.
Speaker 3: They specifically cited applications in fields like weather prediction, ocean science,
Speaker 3: and climate science. And when you look at those fields,
Speaker 3: you realize they are grappling with the exact same computational
Speaker 3: conflict that was paralyzing the Milky Way simulation.
Speaker 2: Hey, let's dig into that analogy for a minute, because
Speaker 2: understanding how this transfers over is vital. How is climate
Speaker 2: modeling analogous to simulating one hundred billion stars?
Speaker 3: Well, take climate science to accurately predict climate trends over
Speaker 3: the next one hundred years. That's your large, slow scale.
Speaker 3: The models need to account for energy and moisture on
Speaker 3: global scale. Yeah, but the physics that actually drive the climate,
Speaker 3: the energy and heat transfer that happens through small fast phenomena.
Speaker 2: Like individual storm cell exactly.
Speaker 3: The formation and evolution of individual thunderstorms, micro level cloud dynamics.
Speaker 3: These are the engines. So if a climate model has
Speaker 3: to use really short time steps, say every fifteen minutes,
Speaker 3: to accurately capture a rapidly forming storm in the Atlantic,
Speaker 3: that same short timestep then has to be applied globally
Speaker 3: across the entire planet's atmosphere and ocean system for every
Speaker 3: fifteen minute interval across one hundred year simulation.
Speaker 2: It's the same paralyzing expense.
Speaker 3: It's the exact same scale disparity problem. The micro event
Speaker 3: dictates the calculation speed for the entire macrosystem. So the
Speaker 3: climate science equivalent of that star cluster particle is probably
Speaker 3: a coarse grid right where small but critical features like
Speaker 3: a sudden temperature inversion or an intense burst of rain
Speaker 3: just get averaged out and lost.
Speaker 2: Absolutely. The resolution is too coarse to capture the nonlinear
Speaker 2: dynamics of those fast local weather events, and that lack
Speaker 2: of resolution introduces error which just compounds over the long
Speaker 2: simulation time, limiting how well you can predict the long
Speaker 2: term climate.
Speaker 3: And you see the same thing in ocean science. How
Speaker 3: so oceanography relies on modeling global currents and deep sea
Speaker 3: circulation over thousands of years. That's the slow, large scale,
Speaker 3: But the physics that drives the mixing of heat and
Speaker 3: salinity and nutrients, which are vital for the global climate,
Speaker 3: that occurs at the micro level through turbulence and little
Speaker 3: localized eddies.
Speaker 2: So you have to model the tiny swirls to understand
Speaker 2: the global current.
Speaker 3: You do if the simulation has to resolve every tiny
Speaker 3: turbulent eddy across the entire planet's oceans, the computational cost
Speaker 3: just explodes. It's the same thirty six year weight, just
Speaker 3: for the oceans instead of the galaxy.
Speaker 2: So the realization here is that this deep learning surgate
Speaker 2: model approach, it's a universal template. Instead of training the
Speaker 2: AI on a supernova, a climate researcher could train it
Speaker 2: on the dynamics of a single funder head, or.
Speaker 3: An oceanographer could train it on the behavior of small
Speaker 3: scale turbulence. That's the core power of this innovation. They
Speaker 3: took a problem that was fundamentally insoluble because of this
Speaker 3: scale conflict, and they strategically offloaded the hardest, shortest time
Speaker 3: scale physics to an AI that was taught the result
Speaker 3: of that physics, rather than being forced to calculate it
Speaker 3: over and over from scratch.
Speaker 2: It's a fundamental shift.
Speaker 3: It transforms AI from just a data analysis tool into
Speaker 3: what the researchers themselves called a genuine tool for scientific discovery.
Speaker 3: It's a mechanism for accelerating the scientific method itself.
Speaker 2: And that quote from Kayehiroshima really summarizes the importance of this.
Speaker 2: He said that integrating AI with high performance computing marks
Speaker 2: a fundamental shift in how we tackle multi scale, multiphysics
Speaker 2: problems across the computational sciences. It implies that so many
Speaker 2: of the computational bottlenecks we just assume or fundamental might
Speaker 2: not be They might just be solvable. With this kind
Speaker 2: of strategic delegation to intelligent systems, we've covered some monumental
Speaker 2: ground today. We trace the challenge facing astrophysicists how to
Speaker 2: simulate the one hundred billion stars of the Milky Way
Speaker 2: with individ dual fidelity, a task so complex it conventionally
Speaker 2: would have taken over thirty six years of real time
Speaker 2: to run just one simulation.
Speaker 3: And the solution wasn't a faster supercomputer, but smarter math
Speaker 3: the deep learning Zurrogate model. By training an AI to
Speaker 3: instantaneously predict the outcome of a supernova, a sidestep that
Speaker 3: computational wall entirely.
Speaker 2: The payoff is immediate and profound. That thirty six year
Speaker 2: waiting period was slashed to just one hundred and fifteen days,
Speaker 2: giving scientists unprecedented insight into how our galaxy evolved and critically,
Speaker 2: how the elements that form all of us spread throughout
Speaker 2: the cosmos.
Speaker 3: And this leads us to the final provocative thought. This breakthrough,
Speaker 3: which was born from the desire to map the entire cosmos,
Speaker 3: might have its most immediate and urgent application right here.
Speaker 2: On Earth in solving our most pressing multi scale problems
Speaker 2: like climate change and weather prediction.
Speaker 3: Exactly if integrating AI with high performance computing can cut
Speaker 3: down thirty six years of cosmological simulation time to less
Speaker 3: than four months, what kind of acceleration can we now
Speaker 3: realistically expect in our ability to model, predict, and ultimately
Speaker 3: prepare for planetary changes. The fundamental limitation that governed our
Speaker 3: pace of understanding the universe has in a way been removed,
Speaker 3: and that raises a really important question for all of
Speaker 3: us about the immediate earth bound impact of this astronomical discovery.
Speaker 3: The SI