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  • richardmitnick 6:15 pm on May 22, 2019 Permalink | Reply
    Tags: , , , , Sandia Z machine,   

    From ASCR Discovery: “Lessons machine-learned” 

    From ASCR Discovery
    ASCR – Advancing Science Through Computing

    From ASCR Discovery

    May 2019

    1
    The University of Arizona’s Joshua Levine is using his Department of Energy Early Career Research Program award to combine machine learning and topology data-analysis tools to better understand trends within climate simulations. These map pairs represent data from January 1950 (top) and January 2010. The left panels depict near-surface air temperatures from hot (red) to cool (blue). In the multicolored images, Levine has used topological, or shape-based, data analysis to organize and color-code the temperature data into a tree-like hierarchy. As the time passes, the data behavior around the North Pole (right panels) breaks into smaller chunks. These changes highlight the need for machine-learning tools to understand how these structures evolve over time. Images courtesy of Joshua Levine, University of Arizona, with data from CMIP6/ESGF.

    Quantifying the risks buried nuclear waste pose to soil and water near the Department of Energy’s (DOE’s) Hanford site in Washington state is not easy. Researchers can’t measure the earth’s permeability, a key factor in how far chemicals might travel, and mathematical models of how substances move underground are incomplete, says Paris Perdikaris of the University of Pennsylvania.

    But where traditional experimental and computational tools fall short, artificial intelligence algorithms can help, building their own inferences based on patterns in the data. “We can’t directly measure the quantities we’re interested in,” he says. “But using this underlying mathematical structure, we can construct machine-learning algorithms that can predict what we care about.”

    Perdikaris’ project is one of several sponsored by the DOE Early Career Research Program that apply machine-learning methods. One piece of his challenge is combining disparate data types such as images, simulations and time-resolved sensor information to find patterns. He will also constrain these models using physics and math, so the resulting predictions respect the underlying science and don’t make spurious connections based on data artifacts. “The byproduct of this is that you can significantly reduce the amount of data you need to make robust predictions. So you can save a lot in data efficiency terms.”

    Another key obstacle is quantifying the uncertainty within these calculations. Missing aspects of the physical model or physical data can affect the prediction’s quality. Besides studying subsurface transport, such algorithms could also be useful for designing new materials.

    Machine learning belongs to a branch of artificial intelligence algorithms that already support our smartphone assistants, manage our home devices and curate our movie and music playlists. Many machine-learning algorithms depend on tools known as neural networks, which mimic the human brain’s ability to filter, classify and draw insights from the patterns within data. Machine-learning methods could help scientists interpret a range of information. In some disciplines, experiments generate more data than researchers can hope to analyze on their own. In others, scientists might be looking for insights about their data and observations.

    But industry’s tools alone won’t solve science’s problems. Today’s machine-learning algorithms, though powerful, make inferences researchers can’t verify against established theory. And such algorithms might flag experimental noise as meaningful. But with algorithms designed to handle science’s tenets, machine learning could boost computational efficiency, allow researchers to compare, integrate and improve physical models, and shift the ways that scientists work.

    Much of industrial artificial intelligence work started with distinguishing, say, cats from Corvettes – analyzing millions of digital images in which data are abundant and have regular, pixelated structures. But with science, researchers don’t have the same luxury. Unlike the ubiquitous digital photos and language snippets that have powered image and voice recognition, scientific data can be expensive to generate, such as in molecular research experiments or large-scale simulations, says Argonne National Laboratory’s Prasanna Balaprakash.

    With his early-career award, he’s designing machine-learning methods that incorporate scientific knowledge. “How do we leverage that? How do we bring in the physics, the domain knowledge, so that an algorithm doesn’t need a lot of data to learn?” He’s also focused on adapting machine-learning algorithms to accept a wider range of data types, including graph-like structures used for encoding molecules or large-scale traffic network scenarios.

    Balaprakash also is exploring ways to automate the development of new machine-learning algorithms on supercomputers – a neural network for designing new neural networks. Writing these algorithms requires a lot of trial-and-error work, and a neural network built with one data type often can’t be used on a new data type.

    Although some fields have data bottlenecks, in other situations scientific instruments generate gobs of data – gigabytes, even petabytes, of results that are beyond human capability to review and analyze. Machine learning could help researchers sift this information and glean important insights. For example, experiments on Sandia National Laboratories’ Z machine, which compresses energy to produce X-rays and to study nuclear fusion, spew out data about material properties under these extreme conditions.

    Sandia Z machine

    When superheated, samples studied in the Z machine mix in a complex process that researchers don’t fully understand yet, says Sandia’s Eric Cyr. He’s exploring data-driven algorithms that can divine an initial model of this mixing, giving theoretical physicists a starting point to work from. In addition, combining machine-learning tools with simulation data could help researchers streamline their use of the Z machine, reducing the number of experiments needed to achieve accurate results and minimizing costs.

    To reach that goal, Cyr focuses on scalable machine algorithms, a technology known as layer-parallel methods. Today’s machine-learning algorithms have expanded from a handful of processing layers to hundreds. As researchers spread these layers over multiple graphics processing units (GPUs), the computational efficiency eventually breaks down. Cyr’s algorithms would split the neural-network layers across processors as the algorithm trains on the problem of interest, he says. “That way if you want to double the number of layers, basically make your neural network twice as deep, you can use twice as many processors and do it in the same amount of time.”

    With problems such as climate and weather modeling, researchers struggle to incorporate the vast range of scales, from globe-circling currents to local eddies. To tackle this problem, Oklahoma State University’s Omer San will apply machine learning to study turbulence in these types of geophysical flows. Researchers must construct a computational grid to run these simulations, but they have to define the scale of the mesh, perhaps 100 kilometers across, to encompass the globe and produce a calculation of manageable size. At that scale, it’s impossible to simulate a range of smaller factors, such as vortices just a few meters wide that can produce important, outsized effects across the whole system because of nonlinear interactions. Machine learning could provide a way to add back in some of these fine details, San says, like software that sharpens a blurry photo.

    Machine learning also could help guide researchers as they choose from the available closure models, or ways to model smaller-scale features, as they examine various flow types. It could be a decision-support system, San says, using local data to determine whether Model A or Model B is a better choice. His group also is examining ways to connect existing numerical methods within neural networks, to allow those techniques to partially inform the systems during the learning process, rather than doing blind analysis. San wants “to connect all of these dots: physics, numerics and the learning framework.”

    Machine learning also promises to help researchers extend the use of mathematical strategies that already support data analysis. At the University of Arizona, Joshua Levine is combining machine learning with topological data-analysis tools.

    These strategies capture data’s shape, which can be useful for visualizing and understanding climate patterns, such as surface temperatures over time. Levine wants to extend topology, which helps researchers analyze a single simulation, to multiple climate simulations with different parameters to understand them as a whole.

    As climate scientists use different models, they often struggle to figure out which ones are correct. “More importantly, we don’t always know where they agree and disagree,” Levine says. “It turns out agreement is a little bit more tractable as a problem.” Researchers can do coarse comparisons – calculating the average temperature across the Earth and checking the models to see if those simple numbers agree. But that basic comparison says little about what happened within a simulation.

    Topology can help match those average values with their locations, Levine says. “So it’s not just that it was hotter over the last 50 years, but maybe it was much hotter in Africa over the last 50 years than it was in South America.”

    All of these projects involve blending machine learning with other disciplines to capitalize on each area’s relative strengths. Computational physics, for example, is built on well-defined principles and mathematical models. Such models provide a good baseline for study, Penn’s Perdikaris says. “But they’re a little bit sterilized and they don’t directly reflect the complexity of the real world.” By contrast, up to now machine learning has only relied on data and observations, he says, throwing away a scientist’s physical knowledge of the world. “Bridging the two approaches will be key in advancing our understanding and enhancing our ability to analyze and predict complex phenomena in the future.”

    Although Argonne’s Balaprakash notes that machine learning has been oversold in some cases, he also believes it will be a transformative research tool, much like the Hubble telescope was for astronomy. “It’s a really promising research area.”

    See the full article here.


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    ASCRDiscovery is a publication of The U.S. Department of Energy

     
  • richardmitnick 4:54 pm on December 10, 2017 Permalink | Reply
    Tags: , , , , Bridgmanite, , Could super-Earths host geology similar to Earth’s?, Exogeology, Institute of Laser Engineering Osaka University, Laboratoire d' Optique Appliquee Palaiseau France, , , Sandia Z machine,   

    From SA: “The Labs That Forge Distant Planets Here on Earth” 

    Scientific American

    Scientific American

    December 10, 2017
    Shannon Hall

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    Could super-Earths such as the one depicted here host geology similar to Earth’s? Credit: NASA Ames, JPL-Caltech, T. Pyle

    Yingwei Fei and his colleagues had spent a month carefully crafting the three slivers of dense silicate—shiny and round, each sample was less than a millimetre thick. But in early November, it was time to say goodbye. Fei carefully packed the samples, plus a few back-ups, in foam and shipped them from Washington DC to Albuquerque, New Mexico. There, the Z Pulsed Power Facility at Sandia National Laboratories will soon send 26 million amps surging towards the slivers, zapping them, one by one, into dust.

    Sandia Z machine

    The Z machine can replicate the extreme conditions inside detonating nuclear weapons. But Fei, a high-pressure experimental geologist at the Carnegie Institution for Science’s Geophysical Laboratory in Washington DC, has a more otherworldly goal in mind: he hopes to explore how bridgmanite, a mineral found deep beneath Earth’s surface, would behave at the higher temperatures and pressures found inside larger rocky planets beyond the Solar System.

    The experiment is one small contribution to exogeology: a research area that is bringing astronomers, planetary scientists and geologists together to explore what exoplanets might look like, geologically speaking. For many scientists, exogeology is a natural extension of the quest to identify worlds that could support life. Already, astronomers have discovered thousands of exoplanets and collected some of their vital statistics, including their masses and radii. Those that orbit in the habitable, or ‘Goldilocks’, zone—a region around the host star that is temperate enough for water to exist in liquid form—are thought to be particularly life-friendly.

    But Earth has a lot more going for it than its size, mass and favourable orbit, says Cayman Unterborn, an exogeologist at Arizona State University in Tempe. Its churning molten core, for example, creates and sustains a magnetic field that shields the planet’s fragile atmosphere from the solar wind. And the motion of tectonic plates helps regulate global temperatures, by cycling carbon dioxide between rocks and the atmosphere. Exoplanet discoveries keep pouring in. But astronomers are “just now realizing, ‘Well wait, we want to understand these systems a lot more than just stamp collecting’”, Unterborn says. “Bringing geology into the mix is a natural factor.”

    Researchers are using simulations and experiments, such as Fei’s at the Z machine, to learn what kinds of exoplanet might have Earth-like geology. The work could help researchers prioritize which exoplanets to study.

    But the field faces several challenges, not least that mystery still surrounds much of Earth’s geology—such as how and when tectonic activity first began. “It’s a fundamental discovery that changed geology,” says Richard Carlson, a geochemist at the Carnegie Institution. “But we still don’t know why it works the way it does.” What’s more, confirming that an exoplanet actually boasts Earth-like geology could be difficult; astronomers rarely observe these planets directly, and if they do, the planet might be the size of a single pixel in their image.

    Even indirect evidence—or the smallest suggestion—of geological activity could give researchers a more complete picture of these distant worlds, and which ones are the best candidates to search for indications of life. “It’s like if you came across a giant crime scene with very little evidence,” says Sara Seager, an astrophysicist at the Massachusetts Institute of Technology in Cambridge. “You work your hardest to take what little evidence there was and try to piece it together somehow.”

    Turning outwards

    One of the most exciting targets of exoplanetary science has been super-Earths. These rocky planets—with as many as ten times Earth’s mass—have no parallel in the Solar System. But they are now known to be quite common in the Galaxy and, because many are fairly big, they could make easier targets for detailed observation than Earth-sized planets.

    Early studies of super-Earth geology, published about ten years ago, examined what these planets would look like if they were simply scaled-up versions of Earth. But the scorching-hot planet 55 Cancri e, first spotted in 2004, underscored the idea that super-Earths could be quite different. Observations in 2011 revealed the planet to have roughly twice Earth’s radius and a little more than eight times its mass, yielding an average density only marginally higher than Earth’s—and that presented a conundrum.

    If 55 Cancri e had an iron core and silicate mantle, like Earth, it should be more massive given its size. An ocean wrapped around the whole planet would bring 55 Cancri e’s density down to Earth-like levels. But the planet is too hot for water to survive for long; it orbits so close to its host star that the day-side temperature is roughly 2,500 kelvin.

    A resolution came in 2012, when Nikku Madhusudhan, an astronomer then at Yale University in New Haven, Connecticut, and his colleagues decided to take a fresh approach. Previous research had suggested that the planet’s host star has a much higher ratio of carbon to oxygen than the Sun. Stars and their planets are built from the same swirling disk of dust and gas, so it seemed fair to assume that 55 Cancri e would also be carbon-rich. When Madhusudhan accounted for this carbon in his model of the planet’s interior, it produced a match with the mass and radius of the world. “That was a revelation,” says Madhusudhan, now at the University of Cambridge, UK. And such a world would be truly alien. Madhusudhan suspects that its crust could be dominated by graphite; inside the planet, the pressure would probably crush vast amounts of the element into diamond. “It would look pretty radical compared with what we see in the Solar System,” he says.

    A planet made of diamond would fire up the imagination, although 55 Cancri e’s host star might not actually contain as much carbon as thought. Even if it did, astronomers caution against assuming that a planet’s composition matches that of its host star. Seager notes that this idea wouldn’t account well for the variety of planets in the Solar System. “At this point, it’s a reasonable inference, but I think it’s important to realize that it’s not iron-clad,” says Gregory Laughlin, an astronomer at Yale.

    Exoplanet-building

    Exogeologists have embraced this uncertainty, and are trying their best to pin down how distant worlds form and evolve. To get from a list of starting elements to geology, scientists need to know what minerals form, when they melt and how their density changes with pressure and temperature. Those data can be used to simulate how a planet develops from an undifferentiated, molten ball into a layered structure, with minerals forming—and sinking or floating—as the planet cools. “We can build up a mineralogical, let’s say, onion-skin model of what the planet would look like initially,” says Wim van Westrenen, a geologist at the Free University of Amsterdam. Then, he says, researchers can use numerical models to predict how that planet will evolve and whether the migration of materials will be enough to drive plate tectonics.

    To gather information to feed these models, geologists are starting to subject synthetic rocks to high temperatures and pressures to replicate an exoplanet’s innards—as Fei and his colleagues are doing. Although the goal of these experiments is new, the approach is not. For decades, experimental petrologists have built instruments to simulate the conditions of Earth’s interior, anywhere from a few centimetres below the surface to Earth’s core. Many use a device called a diamond anvil cell. This apparatus squeezes materials by pushing the blunted tips of two gem-quality diamonds together. While a sample is under pressure, a laser can be used to heat it. At the same time, experimentalists can bombard the mat­erial with X-rays to investigate its crystalline structure and explore how the material changes as it is pushed to high temperatures and pressures.

    Groups including Sang-Heon Dan Shim, a mineral physicist at Arizona State University, and his colleagues have used this process to squeeze carbon-rich samples that might reflect the composition of 55 Cancri e. The work has revealed how planets dominated by carbon-containing compounds called carbides might transport heat, and how they might differ from planets that, like Earth, are dominated by silicates.

    Carbon is not the only element of interest. Unterborn points to magnesium, silicon and iron as “the big three” that will affect a planet’s bulk structure, influencing how heat flows in the mantle and the relative size of the planet’s core—and so the presence of plate tectonics and a global magnetic field, respectively. Ratios of these elements vary widely in stars. The Sun has one magnesium atom for every silicon atom; in other stars, that ratio ranges from 0.5 to 2. The difference might seem small, but if the same ratios are present in planets, they could drastically affect geology.

    Most textbooks argue that magnesium-rich rocks would be softer than those containing high concentrations of silicon—so much so that walking on a magnesium-rich world might feel like walking on mud. Shim’s diamond-anvil-cell work on rocks with various magnesium-to-silicon ratios suggests these worlds could also boast deeper reservoirs of magma than a silicon-rich planet and, as a result, more catastrophic volcanoes. But Shim notes that other parameters, such as the concentration of water in minerals, must also be taken into account.

    High pressure

    With two diamonds, Shim can apply no more than 400 gigapascals of pressure, a little higher than the pressure in Earth’s core. To probe the interiors of super-Earths, he has turned to the world’s brightest X-ray laser: the Linac Coherent Light Source at SLAC National Accelerator Laboratory in Menlo Park, California.

    SLAC/LCLS

    The instrument can generate shocks inside the sample, producing pressures as high as 600 giga­pascals—enough to simulate the cores of planets twice as massive as Earth.

    Geologists are also using other large facilities to probe potential exoplanet formulations. The Z machine can reach 1,000 gigapascals—the condition expected inside planets nearly three times Earth’s mass. Laser facilities in Palaiseau, France, and Osaka, Japan, can reach a similar range.

    3
    Laboratoire d’ Optique Appliquee, Palaiseau, France

    4
    Institute of Laser Engineering, Osaka University

    And some researchers have turned to the National Ignition Facility at Lawrence Liver­more National Laboratory in California, which is used to study nuclear fusion and can subject samples to as much as 5,000 giga­pascals, the pressure of Jupiter’s deep interior.


    LLNL/NIF

    These experiments are still in their preliminary stages, as researchers compete for time at these facilities and slowly accumulate data on a variety of basic compounds.

    At the end of the day, exogeologists hope to find the right combination of elements to build exoplanets with Earth-like geologies. “I would like to identify the compositional Goldilocks zone,” says Wendy Panero, a geologist at the Ohio State University in Columbus. “What is the not-too-soft, not-too-stiff habitable zone for rock composition?”

    The answer might not be clear-cut. Even perfect knowledge of composition might not tell exogeologists much about the state of a planet. Earth, for example, did not host plate tectonics in its early history, and it is not expected to do so forever. And its neighbour Venus shows how widely planetary evolution can diverge. The planet’s mass, radius, composition and distance from the Sun are similar to those of Earth. But Earth supports life, whereas Venus, swaddled in a haze of carbon dioxide, is quite dead. Stephen Mojzsis, a geologist at the University of Colorado Boulder, suspects that the loss of plate tectonics on Earth will eventually cause it to resemble its super-heated sibling. “It’s inevitable,” he says. “We’re just not sure when that will happen.” So, although most early exoplanet models are focusing on composition, exogeologists might ultimately have to include additional factors such as billions of years of planetary evolution.

    Some expect that this work will help astronomers determine which planets to target in the search for life. If scientists know the conditions needed to sustain a magnetic field for billions of years, or the proportions of elements required to drive convection in the mantle, they could advise their colleagues to scrutinize the worlds that meet those criteria. Then astronomers could turn powerful telescopes, such as NASA’s James Webb Space Telescope, slated to launch in 2019, towards those planets to search their atmospheres for potential signatures of alien life.

    It might also be possible to spot geological activity from a distance. A transient spike in atmospheric sulfur, for example, might be indirect evidence of the presence of an active volcano. Changes in reflectivity as a planet rotates might hint at the presence of continents and oceans, which could also suggest tectonic activity.

    Already, there has been talk of a possible detection of volcanic activity—on 55 Cancri e.

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    55 Cancri e

    In 2016, Brice-Olivier Demory, an astronomer at the University of Bern, and his colleagues published the first heat map of the planet, created using NASA’s infrared Spitzer Space Telescope.

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    http://actualinfo.website/2016/04/02/astronomers-have-compiled-the-first-heat-map-of-super-earths/

    NASA/Spitzer Infrared Telescope

    The planet is tidally locked to its star, so one hemisphere is eternally bathed in sunlight and the other is dark. The planet should be hottest closest to the star, but Demory and his colleagues discovered that the hottest point seems to be offset. They posited that flowing lava is carrying heat away (although more recent work has argued that winds might be responsible instead).

    It’s clear that 55 Cancri e is no place for life. But other worlds may be much more inviting. Earlier this year, Unterborn completed a study that looked at more than 1,000 Sun-like stars. Using their compositions, he determined that one-third of those stars could host planets whose crust was dense enough to sink into the mantle—a process that might let plate tectonics thrive for billions of years.

    Although researchers are just the beginning to explore the geology of exoplanets, Carlson notes that the study of these worlds has already yielded a number of surprises, not least evidence of planets that seem to have undergone dramatic migrations from their original orbits. This discovery caused astronomers to rethink the Solar System’s evolution, and theorize that similar movements could have helped carry materials, such as water ice, to Earth. “I don’t think humans are anywhere near as imaginative and creative as nature is,” Carlson says. “So, understanding the diversity of what’s out there will just open our eyes to other possibilities. And it’s those other possibilities that will help us understand our situation better.”

    See the full article here .

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    Scientific American, the oldest continuously published magazine in the U.S., has been bringing its readers unique insights about developments in science and technology for more than 160 years.

     
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