Computers Math

A robotic bee that can fly fully in all directions has been developed by Washington State University researchers.
With four wings made out of carbon fiber and mylar as well as four light-weight actuators to control each wing, the Bee++ prototype is the first to fly stably in all directions. That includes the tricky twisting motion known as yaw, with the Bee++ fully achieving the six degrees of free movement that a typical flying insect displays.
Led by Néstor O. Pérez-Arancibia, Flaherty associate professor in WSU’s School of Mechanical and Materials Engineering, the researchers report on their work in the journal, IEEE Transactions on Robotics. Pérez-Arancibia will present the results at the IEEE International Conference on Robotics and Automation at the end of this month.
Researchers have been trying to develop artificial flying insects for more than 30 years, said Pérez-Arancibia. They could someday be used for many applications, including for artificial pollination, search and rescue efforts in tight spaces, biological research, or environmental monitoring, including in hostile environments.
But just getting the tiny robots to take off and land required development of controllers that act the way an insect brain does.
“It’s a mixture of robotic design and control,” he said. “Control is highly mathematical, and you design a sort of artificial brain. Some people call it the hidden technology, but without those simple brains, nothing would work.”
Researchers initially developed a two-winged robotic bee, but it was limited in its movement. In 2019, Pérez-Arancibia and two of his PhD students for the first time built a four-winged robot light enough to take off. To do two maneuvers known as pitching or rolling, the researchers make the front wings flap in a different way than the back wings for pitching and the right wings flap in a different way than the left wings for rolling, creating torque that rotates the robot about its two main horizontal axes.

But being able to control the complex yaw motion is tremendously important, he said. Without it, robots spin out of control, unable to focus on a point. Then they crash.
“If you can’t control yaw, you’re super limited,” he said. “If you’re a bee, here is the flower, but if you can’t control the yaw, you are spinning all the time as you try to get there.”
Having all degrees of movement is also critically important for evasive maneuvers or tracking objects.
“The system is highly unstable, and the problem is super hard,” he said. “For many years, people had theoretical ideas about how to control yaw, but nobody could achieve it due to actuation limitations.”
To allow their robot to twist in a controlled manner, the researchers took a cue from insects and moved the wings so that they flap in an angled plane. They also increased the amount of times per second their robot can flap its wings — from 100 to 160 times per second.
“Part of the solution was the physical design of the robot, and we also invented a new design for the controller — the brain that tells the robot what to do,” he said.
Weighing in at 95 mg with a 33-millimeter wingspan, the Bee++ is still bigger than real bees, which weigh around 10 milligrams. Unlike real insects, it can only fly autonomously for about five minutes at a time, so it is mostly tethered to a power source through a cable. The researchers are also working to develop other types of insect robots, including crawlers and water striders.
Pérez-Arancibia’s former PhD students at the University of Southern California, Ryan M. Bena, Xiufeng Yang, and Ariel A. Calderón, co-authored the article. The work was funded by the National Science Foundation and DARPA. The WSU Foundation and the Palouse Club through WSU’s Cougar Cage program has also provided support. More

A quarter of a century ago, theoretical physicists at the University of Innsbruck made the first proposal on how to transmit quantum information via quantum repeaters over long distances which would open the door to the construction of a worldwide quantum information network. Now, a new generation of Innsbruck researchers has built a quantum repeater node for the standard wavelength of telecommunication networks and transmitted quantum information over tens of kilometers.
Quantum networks connect quantum processors or quantum sensors with each other. This allows tap-proof communication and high-performance distributed sensor networks. Between network nodes, quantum information is exchanged by photons that travel through optical waveguides. Over long distances, however, the likelihood of photons being lost increases dramatically. As quantum information cannot simply be copied and amplified, 25 years ago Hans Briegel, Wolfgang Dür, Ignacio Cirac and Peter Zoller, then all at the University of Innsbruck, provided the blueprints for a quantum repeater. These feature light-matter entanglement sources and memories to create entanglement in independent network links that are connected between them by a so-called entanglement swap to finally distribute entanglement over long distances.
Even transmission over 800 kilometers possible
Quantum physicists led by Ben Lanyon from the Department of Experimental Physics at the University of Innsbruck have now succeeded in building the core parts of a quantum repeater — a fully functioning network node made with two single matter systems enabling entanglement creation with a photon at the standard frequency of the telecommunications network and entanglement swapping operations. The repeater node consists of two calcium ions captured in an ion trap within an optical resonator as well as single photon conversion to the telecom wavelength. The scientists thus demonstrated the transfer of quantum information over a 50-kilometer-long optical fiber, with the quantum repeater placed exactly halfway between starting and end point. The researchers were also able to calculate which improvements of this design would be necessary to make transmission over 800 kilometers possible which would allow to connect Innsbruck to Vienna.
The current results were published in Physical Review Letters. Funding for the research was provided by a START award from the Austrian Science Fund FWF, the Austrian Academy of Sciences and the European Union, among others. Lanyon’s team is part of the Quantum Internet Alliance, an international project under the EU Quantum Flagship. More

Cryptocurrency transactions may be costing more than just transaction fees. The electricity used for these transactions is more than what some countries, like Argentina and Australia, use in an entire year.
Published estimates of the total global electricity usage for cryptocurrency assets such as Bitcoin are between 120 and 240 billion kilowatt-hours per year, according to the White House Office of Science and Technology. The United States leads these numbers.
Finance and business experts have debated the ramifications of cryptocurrency and mining, but little focus has been placed on the impact of these activities on the power grid and energy consumption until now.
Dr. Le Xie, professor in the Department of Electrical and Computer Engineering at Texas A&M University and associate director of the Texas A&M Energy Institute, is at the center of this effort to understand how cryptocurrency mining impacts the power grid and how to use this information for further research, education and policymaking.
Even as technology improves, allowing users to do more while using less energy, cryptocurrency mining is computationally intensive, and the measure of power on the blockchain network, or hash rate, is still rising.
During the summer heatwave of 2022 in Texas, Xie and his collaborators found an 18% reduction in worldwide cryptocurrency mining. The decrease was linked to the stress on the Texas power grid, which led the Electric Reliability Council of Texas to issue a request for energy consumers to conserve energy.

“There seems to be a very strong negative correlation between the mining demand and the systemwide total net demand,” Xie said. “When the grid is stressed, crypto miners are shutting down, which demonstrates a potential for demand flexibility.”
For example, when the grid is under stress due to a heat wave, homeowners consume more air conditioning and, in turn, more power. Compared to these types of firm demand, the cryptocurrency mining demand shows good potential for providing flexibilities during times when peak energy usage in other areas is vital.
Their findings are published in the March issue of the Institute of Electrical and Electronics Engineers Transactions on Energy Markets, Policy and Regulation and the June issue of Advances in Applied Energy.
In these papers, Xie and his students provide data to allow a first step into studying these mining facilities’ carbon footprint and the impact on grid reliability and wholesale electricity prices. Ultimately, location matters, and many factors play a part in this complex discussion.
“Increasing firm demand will invariably result in a decrease in grid reliability,” Xie said. “However, with crypto mining modeled as a flexible load that can be turned off during the stressed moments, it can be a positive contributor to the grid reliability.”
Xie is the lead for the Blockchain and Energy Research Consortium at Texas A&M, which is a collaboration between a team of Texas A&M researchers and industry partners. Their mission is to provide an unbiased multidisciplinary resource to communicate recent developments in the intersection of blockchain and energy.
Although cryptocurrency is still in its infancy, one thing is certain — increasing energy usage will be critical as this emerging industry for transactions continues to advance. With that in mind, Xie is continuing his research to find a solution that helps take advantage of blockchain-enabled technologies while ensuring a sustainable grid operation. More

A University of Minnesota Twin Cities-led team has developed a first-of-its-kind, breakthrough method that makes it easier to create high-quality metal oxide thin films out of “stubborn” metals that have historically been difficult to synthesize in an atomically precise manner. This research paves the way for scientists to develop better materials for various next-generation applications including quantum computing, microelectronics, sensors, and energy catalysis.
The researchers’ paper is published in Nature Nanotechnology, a peer-reviewed, scientific journal run by Nature Publishing Group.
“This is truly remarkable discovery, as it unveils an unparalleled and simple way for navigating material synthesis at the atomic scale by harnessing the power of epitaxial strain,” said Bharat Jalan, senior author on the paper and a professor and Shell Chair in the University of Minnesota Department of Chemical Engineering and Materials Science. “This breakthrough represents a significant advancement with far-reaching implications in a broad range of fields. Not only does it provide a means to achieve atomically-precise synthesis of quantum materials, but it also holds immense potential for controlling oxidation-reduction pathways in various applications, including catalysis and chemical reactions occurring in batteries or fuel cells.”
“Stubborn” metals oxides, such as those based on ruthenium or iridium, play a crucial role in numerous applications in quantum information sciences and electronics. However, converting them into thin films has been a challenge for researchers due to the inherent difficulties in oxidizing metals using high-vacuum processes.
The fabrication of these materials has perplexed materials scientists for decades. While some researchers have successfully achieved oxidation, the methods used thus far have been costly, unsafe, or have resulted in poor material quality.
The University of Minnesota researchers’ solution? Give it a stretch.

While attempting to synthesize metal oxides using conventional molecular beam epitaxy, a low-energy technique that generates single layers of material in an ultra-high vacuum chamber, the researchers stumbled upon a groundbreaking revelation. They found that incorporating a concept called “epitaxial strain” — effectively stretching the metals at the atomic level — significantly simplifies the oxidation process of these stubborn metals.
“This enables the creation of technologically important metal oxides out of stubborn metals in ultra-high vacuum atmospheres, which has been a longstanding problem,” said Sreejith Nair, first author of the paper and a University of Minnesota chemical engineering Ph.D. student. “The current synthesis approaches have limits, and we need to find new ways to push those limits further so that we can make better quality materials. Our new method of stretching the material at the atomic scale is one way to improve the performance of the current technology.”
Although the University of Minnesota team used iridium and ruthenium as examples in this paper, their method has the potential to generate atomically-precise oxides of any hard-to-oxidize metal. With this groundbreaking discovery, the researchers aim to empower scientists worldwide to synthesize these novel materials.
The researchers worked closely with collaborators at Auburn University, the University of Delaware, Brookhaven National Laboratory, Argonne National Laboratory, and fellow University of Minnesota Department of Chemical Engineering and Materials Science Professor Andre Mkhoyan’s lab to verify their method.
“When we looked at these metal oxide films very closely using very powerful electron microscopes, we captured the arrangements of the atoms and determined their types,” Mkhoyan explained. “Sure enough, they were nicely and periodically arranged as they should be in these crystalline films.”
This research was funded primarily by the United States Department of Energy (DOE), the Air Force Office of Scientific Research (AFOSR), and the University of Minnesota’s Materials Research Science and Engineering Center (MRSEC).
In addition to Jalan, Nair, and Mkhoyan, the research team included University of Minnesota Twin Cities researchers Zhifei Yang, Dooyong Lee, and Silu Guo; Brookhaven National Laboratory researcher Jerzy Sadowski; Auburn University researchers Spencer Johnson, Ryan Comes, and Wencan Jin; University of Delaware researchers Abdul Saboor and Anderson Janotti; and Argonne National Laboratory researchers Yan Li and Hua Zhou. Parts of the work were carried out at the University of Minnesota’s Characterization Facility. More

Artificial intelligence has exploded across our news feeds, with ChatGPT and related AI technologies becoming the focus of broad public scrutiny. Beyond popular chatbots, biologists are finding ways to leverage AI to probe the core functions of our genes.
Previously, University of California San Diego researchers who investigate DNA sequences that switch genes on used artificial intelligence to identify an enigmatic puzzle piece tied to gene activation, a fundamental process involved in growth, development and disease. Using machine learning, a type of artificial intelligence, School of Biological Sciences Professor James T. Kadonaga and his colleagues discovered the downstream core promoter region (DPR), a “gateway” DNA activation code that’s involved in the operation of up to a third of our genes.
Building from this discovery, Kadonaga and researchers Long Vo ngoc and Torrey E. Rhyne have now used machine learning to identify “synthetic extreme” DNA sequences with specifically designed functions in gene activation. Publishing in the journal Genes & Development, the researchers tested millions of different DNA sequences through machine learning (AI) by comparing the DPR gene activation element in humans versus fruit flies (Drosophila). By using AI, they were able to find rare, custom-tailored DPR sequences that are active in humans but not fruit flies and vice versa. More generally, this approach could now be used to identify synthetic DNA sequences with activities that could be useful in biotechnology and medicine.
“In the future, this strategy could be used to identify synthetic extreme DNA sequences with practical and useful applications. Instead of comparing humans (condition X) versus fruit flies (condition Y) we could test the ability of drug A (condition X) but not drug B (condition Y) to activate a gene,” said Kadonaga, a distinguished professor in the Department of Molecular Biology. “This method could also be used to find custom-tailored DNA sequences that activate a gene in tissue 1 (condition X) but not in tissue 2 (condition Y). There are countless practical applications of this AI-based approach. The synthetic extreme DNA sequences might be very rare, perhaps one-in-a-million — if they exist they could be found by using AI.”
Machine learning is a branch of AI in which computer systems continually improve and learn based on data and experience. In the new research, Kadonaga, Vo ngoc (a former UC San Diego postdoctoral researcher now at Velia Therapeutics) and Rhyne (a staff research associate) used a method known as support vector regression to “train” machine learning models with 200,000 established DNA sequences based on data from real-world laboratory experiments. These were the targets presented as examples for the machine learning system. They then “fed” 50 million test DNA sequences into the machine learning systems for humans and fruit flies and asked them to compare the sequences and identify unique sequences within the two enormous data sets.
While the machine learning systems showed that human and fruit fly sequences largely overlapped, the researchers focused on the core question of whether the AI models could identify rare instances where gene activation is highly active in humans but not in fruit flies. The answer was a resounding “yes.” The machine learning models succeeded in identifying human-specific (and fruit fly-specific) DNA sequences. Importantly, the AI-predicted functions of the extreme sequences were verified in Kadonaga’s laboratory by using conventional (wet lab) testing methods.
“Before embarking on this work, we didn’t know if the AI models were ‘intelligent’ enough to predict the activities of 50 million sequences, particularly outlier ‘extreme’ sequences with unusual activities. So, it’s very impressive and quite remarkable that the AI models could predict the activities of the rare one-in-a-million extreme sequences,” said Kadonaga, who added that it would be essentially impossible to conduct the comparable 100 million wet lab experiments that the machine learning technology analyzed since each wet lab experiment would take nearly three weeks to complete.
The rare sequences identified by the machine learning system serve as a successful demonstration and set the stage for other uses of machine learning and other AI technologies in biology.
“In everyday life, people are finding new applications for AI tools such as ChatGPT. Here, we’ve demonstrated the use of AI for the design of customized DNA elements in gene activation. This method should have practical applications in biotechnology and biomedical research,” said Kadonaga. “More broadly, biologists are probably at the very beginning of tapping into the power of AI technology.” More

The number of qubits in superconducting quantum computers has risen rapidly during the last years, but further growth is limited by the need for ultra-cold operating temperatures. Connecting several smaller processors could create larger, more computationally powerful quantum computers, however doing so poses new challenges. A team of researchers led by Rishabh Sahu, Liu Qiu, and Johannes Fink from the Institute of Science and Technology Austria (ISTA) have now, for the first time, demonstrated quantum entanglement between optical and microwave photons that could lay the foundation for such a future quantum network.
Quantum computers promise to solve challenging tasks in material science and cryptography that will remain out of reach even for the most powerful conventional supercomputers in the future. Yet, this will likely require millions of high-quality qubits due to the required error correction.
Progress in superconducting processors advances quickly with a current qubit count in the few hundreds. The advantages of this technology are the fast computing speed and its compatibility with microchip fabrication, but the need for ultra-cold temperatures ultimately confines the processor in size and prevents any physical access once it is cooled down.
A modular quantum computer with multiple separately cooled processor nodes could solve this. However, single microwave photons — the particles of light that are the native information carriers between superconducting qubits within the processors — are not suitable to be sent through a room temperature environment between the processors. The world at room temperature is bustling with heat, which easily disturbs the microwave photons and their fragile quantum properties like entanglement.
Researchers from the Fink group at the Institute of Science and Technology Austria (ISTA), together with collaborators from TU Wien and the Technical University of Munich, demonstrated an important technological step to overcome these challenges. They entangled low-energy microwave with high-energy optical photons for the very first time. Such an entangled quantum state of two photons is the foundation to wire up superconducting quantum computers via room temperature links. This has implications not only for scaling up existing quantum hardware but it is also needed to realize interconnects to other quantum computing platforms as well as for novel quantum-enhanced remote sensing applications. Their results have been published in the journal Science.
Cooling Away the Noise
Rishabh Sahu, a postdoc in the Fink group and one of the first authors of the new study, explains, “One major problem for any qubit is noise. Noise can be thought of as any disturbance to the qubit. One major source of noise is the heat of the material the qubit is based on.”

Heat causes atoms in a material to jostle around rapidly. This is disruptive to quantum properties like entanglement, and as a result, it would make qubits unsuitable for computation. Therefore, to remain functional, a quantum computer must have its qubits isolated from the environment, cooled to extremely low temperatures, and kept within a vacuum to preserve their quantum properties.
For superconducting qubits, this happens in a special cylindrical device that hangs from the ceiling, called a “dilution refrigerator” in which the “quantum” part of the computation takes place. The qubits at its very bottom are cooled down to only a few thousandths of a degree above absolute zero temperature — at about -273 degrees Celsius. Sahu excitedly adds, “This makes these fridges in our labs the coldest locations in the whole universe, even colder than space itself.”
The refrigerator has to continuously cool the qubits but the more qubits and associated control wiring are added, the more heat is generated and the harder it is to keep the quantum computer cool. “The scientific community predicts that at around 1,000 superconducting qubits in a single quantum computer, we reach the limits of cooling,” Sahu cautions. “Just scaling up is not a sustainable solution to construct more powerful quantum computers.”
Fink adds, “Larger machines are in development but each assembly and cooldown then becomes comparable to a rocket launch, where you only find out about problems once the processor is cold and without the ability to intervene and correct such problems.”
Quantum Waves
“If a dilution fridge cannot sufficiently cool more than a thousand superconducting qubits at once, we need to link several smaller quantum computers to work together,” Liu Qiu, postdoc in the Fink group and another first author of the new study, explains. “We would need a quantum network.”

Linking together two superconducting quantum computers, each with its own dilution refrigerator, is not as straightforward as connecting them with an electrical cable. The connection needs special consideration to preserve the quantum nature of the qubits.
Superconducting qubits work with tiny electrical currents that move back and forth in a circuit at frequencies about ten billion times per second. They interact using microwave photons — particles of light. Their frequencies are similar to the ones used by cellphones.
The problem is that even a small amount of heat would easily disturb single microwave photons and their quantum properties needed to connect the qubits in two separate quantum computers. When passing through a cable outside the refrigerator, the heat of the environment would render them useless.
“Instead of the noise-prone microwave photons that we need to do the computations within the quantum computer, we want to use optical photons with much higher frequencies similar to visible light to network quantum computers together,” Qiu explains. These optical photons are the same kind sent through optical fibers that deliver high-speed internet to our homes. This technology is well understood and much less susceptible to noise from heat. Qiu adds, “The challenge was how to have the microwave photons interact with the optical photons and how to entangle them.”
Splitting Light
In their new study, the researchers used a special electro-optic device: an optical resonator made from a nonlinear crystal, which changes its optical properties in the presence of an electric field. A superconducting cavity houses this crystal and enhances this interaction.
Sahu and Qiu used a laser to send billions of optical photons into the electro-optic crystal for a fraction of a microsecond. In this way, one optical photon splits into a pair of new entangled photons: an optical one with only slightly less energy than the original one and a microwave photon with much lower energy.
“The challenge of this experiment was that the optical photons have about 20,000 times more energy than the microwave photons,” Sahu explains, “and they bring a lot of energy and therefore heat into the device that can then destroy the quantum properties of the microwave photons. We have worked for months tweaking the experiment and getting the right measurements.” To solve this problem, the researchers built a bulkier superconducting device compared to previous attempts. This not only avoids a breakdown of superconductivity, but it also helps to cool the device more effectively and to keep it cold during the short timescales of the optical laser pulses.
“The breakthrough is that the two photons leaving the device — the optical and the microwave photon — are entangled,” Qiu explains. “This has been verified by measuring correlations between the quantum fluctuations of the electromagnetic fields of the two photons that are stronger than can be explained by classical physics.”
“We are now the first to entangle photons of such vastly different energy scales.” Fink says, “This is a key step to creating a quantum network and also useful for other quantum technologies, such as quantum-enhanced sensing.” More

A new paper on wireless connectivity from the lab of Dinesh Bharadia, an affiliate of the UC San Diego Qualcomm Institute (QI) and faculty member with the Jacobs School of Engineering’s Department of Electrical and Computer Engineering, introduces a new technique for increasing access to the 5G-and-beyond millimeter wave (mmWave) network.
“Energy grids and mmWave/sub-THz networks share a remarkable similarity; both face fundamental challenges in efficient distribution,” said Bharadia. “Just as energy grids generate substantial amounts of energy but encounter significant hurdles in efficiently delivering it to homes, the utilization of mmWave/sub-THz networks for seamless data connectivity presents a similar predicament. Despite abundantly available bandwidth in these spectra, the efficient distribution of data with these spectra to user devices remains a formidable challenge.”
The paper, “mmFlexible: Flexible Directional Frequency Multiplexing for Multi-user mmWave Networks,” was presented by Ph.D. student and lead author Ish Kumar Jain at the IEEE International Conference on Computer Communications in New York on Wednesday, May 17.
Sharing Access
With the introduction of more automation and greater speeds and processing power behind wireless networks, the infrastructure that connects people to these resources has fallen behind.
Jain was drawn to the challenge of creating a device that could bridge this gap and give people greater access to the 5G mmWave network.

5G mmWave systems use radio frequencies to connect everything from “smart” cars to handheld devices and virtual reality sets to wireless networks. The advancement from 4G to 5G allows for higher speeds and bandwidth overall.
Part of the problem, Jain says, is that the jump from 4G to 5G opened up far more resources and processing power than existing infrastructure could handle. mmWave systems depend on a “pencil beam” distribution model in which a base station sends out a single beam of coverage, like shining a light in the dark. Everyone within that beam has access to all resources that the 5G mmWave network has to offer, regardless of whether their devices can process them.
This can lead to a waste of bandwidth that might otherwise have been leveraged by users in other regions. Even shifting this beam, like a lighthouse rotating slowly at timed intervals, creates lag for those who fall beyond its range.
To address the combined issues of wasted bandwidth and lag, Jain, Rohith Reddy Vennam and Raghav Subbaraman, also Ph.D. students in Bharadia’s Wireless Communication, Sensing and Networking Group (WCSNG), set out to determine whether they could create an antenna array that served users in multiple directions without sacrificing distance and power.
The team designed a prototype device that works in concert with a novel array of antennas to divide a single frequency band into multiple usable beams. Called a delay phased array, this antenna arrangement leverages 5G mmWave’s sheer amount of bandwidth to connect multiple regions to the network and can be tailored to deliver greater connection to those who need it.
This new, programmable array can also be built using existing technologies and scaled up with a very high number of antennas to support all future devices.
Through experiments run in QI’s Atkinson Hall on the UC San Diego campus, the team found mmFlexible decreased lag by 60-150%.
“It’s very exciting to see new generations of applications coming up,” said Jain. “But I feel, in the future, the number of [wireless] devices will grow and so will their demand for wireless spectrum. These are the key things that motivate me to further explore these innovative techniques.” More

To study ocean currents, scientists release GPS-tagged buoys in the ocean and record their velocities to reconstruct the currents that transport them. These buoy data are also used to identify “divergences,” which are areas where water rises up from below the surface or sinks beneath it.
By accurately predicting currents and pinpointing divergences, scientists can more precisely forecast the weather, approximate how oil will spread after a spill, or measure energy transfer in the ocean. A new model that incorporates machine learning makes more accurate predictions than conventional models do, a new study reports.
A multidisciplinary research team including computer scientists at MIT and oceanographers has found that a standard statistical model typically used on buoy data can struggle to accurately reconstruct currents or identify divergences because it makes unrealistic assumptions about the behavior of water.
The researchers developed a new model that incorporates knowledge from fluid dynamics to better reflect the physics at work in ocean currents. They show that their method, which only requires a small amount of additional computational expense, is more accurate at predicting currents and identifying divergences than the traditional model.
This new model could help oceanographers make more accurate estimates from buoy data, which would enable them to more effectively monitor the transportation of biomass (such as Sargassum seaweed), carbon, plastics, oil, and nutrients in the ocean. This information is also important for understanding and tracking climate change.
“Our method captures the physical assumptions more appropriately and more accurately. In this case, we know a lot of the physics already. We are giving the model a little bit of that information so it can focus on learning the things that are important to us, like what are the currents away from the buoys, or what is this divergence and where is it happening?” says senior author Tamara Broderick, an associate professor in MIT’s Department of Electrical Engineering and Computer Science (EECS) and a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society.

Broderick’s co-authors include lead author Renato Berlinghieri, an electrical engineering and computer science graduate student; Brian L. Trippe, a postdoc at Columbia University; David R. Burt and Ryan Giordano, MIT postdocs; Kaushik Srinivasan, an assistant researcher in atmospheric and ocean sciences at the University of California at Los Angeles; Tamay Özgökmen, professor in the Department of Ocean Sciences at the University of Miami; and Junfei Xia, a graduate student at the University of Miami. The research will be presented at the International Conference on Machine Learning.
Diving into the data
Oceanographers use data on buoy velocity to predict ocean currents and identify “divergences” where water rises to the surface or sinks deeper.
To estimate currents and find divergences, oceanographers have used a machine-learning technique known as a Gaussian process, which can make predictions even when data are sparse. To work well in this case, the Gaussian process must make assumptions about the data to generate a prediction.
A standard way of applying a Gaussian process to oceans data assumes the latitude and longitude components of the current are unrelated. But this assumption isn’t physically accurate. For instance, this existing model implies that a current’s divergence and its vorticity (a whirling motion of fluid) operate on the same magnitude and length scales. Ocean scientists know this is not true, Broderick says. The previous model also assumes the frame of reference matters, which means fluid would behave differently in the latitude versus the longitude direction.

“We were thinking we could address these problems with a model that incorporates the physics,” she says.
They built a new model that uses what is known as a Helmholtz decomposition to accurately represent the principles of fluid dynamics. This method models an ocean current by breaking it down into a vorticity component (which captures the whirling motion) and a divergence component (which captures water rising or sinking).
In this way, they give the model some basic physics knowledge that it uses to make more accurate predictions.
This new model utilizes the same data as the old model. And while their method can be more computationally intensive, the researchers show that the additional cost is relatively small.
Buoyant performance
They evaluated the new model using synthetic and real ocean buoy data. Because the synthetic data were fabricated by the researchers, they could compare the model’s predictions to ground-truth currents and divergences. But simulation involves assumptions that may not reflect real life, so the researchers also tested their model using data captured by real buoys released in the Gulf of Mexico.
In each case, their method demonstrated superior performance for both tasks, predicting currents and identifying divergences, when compared to the standard Gaussian process and another machine-learning approach that used a neural network. For example, in one simulation that included a vortex adjacent to an ocean current, the new method correctly predicted no divergence while the previous Gaussian process method and the neural network method both predicted a divergence with very high confidence.
The technique is also good at identifying vortices from a small set of buoys, Broderick adds.
Now that they have demonstrated the effectiveness of using a Helmholtz decomposition, the researchers want to incorporate a time element into their model, since currents can vary over time as well as space. In addition, they want to better capture how noise impacts the data, such as winds that sometimes affect buoy velocity. Separating that noise from the data could make their approach more accurate.
“Our hope is to take this noisily observed field of velocities from the buoys, and then say what is the actual divergence and actual vorticity, and predict away from those buoys, and we think that our new technique will be helpful for this,” she says.
This research is supported, in part, by the Office of Naval Research, a National Science Foundation (NSF) CAREER Award, and the Rosenstiel School of Marine, Atmospheric, and Earth Science at the University of Miami. More