Computer modeling

MIT researchers have developed the Probabilistic Bias Correction (PBC) tool, which “uses machine learning to identify systematic bias in a model and make corrections” to improve weather forecasts, reports The Verge’s Justine Calma. “The researchers found that PBC improved the accuracy of a leading physics-based weather model by the European Centre for Medium-Range Weather Forecasts (ECMWF) for 98 percent of precipitation and 92 percent of temperature targets,” notes Calma. “The machine learning framework also doubled the accuracy of ECMWF’s new AI forecasting system in the study’s analysis.” 

Gizmodo’s Ellyn Lapointe spotlights the Probabilistic Bias Correction (PBC) tool developed by MIT researchers to “correct systemic errors in forecast models and improve the accuracy of their subseasonal outlooks.” “Extreme weather has the largest impact on our society and improved forecasts of these events can yield large societal benefits,” says Research Scientist Judah Cohen. “Interestingly, previous bias correction schemes actually made forecasts of extreme weather worse, and our model showed clear improvement of extreme weather forecasts.”  

MIT researchers have developed an AI system “that not only minimizes forecasting errors, but learns from its inevitable mistakes through a method called postprocessing,” writes reporter Andrew Paul for Popular Science. “Their AI postprocessing integration blew the existing forecasting models out of the water,” notes Paul. “Such massive improvements could soon help the farmers and energy providers who already rely on subseasonal forecasts, but the benefits may even save lives.” 

Research Scientist Julian Klein and Prof. Frances Ross speak with speak with Physics reporter Akashni Raghubar Latchanna about an electron microscopy technique they developed to move atoms within a material, creating new possibilities for engineering electrical, optical, and quantum properties. “If we can control the electronic or optical functions of the materials, we might potentially make those devices more brain-like, more portable, and less power-hungry,” Ross said. 

Prof. Tom Leighton, CEO of Akamai, speaks to Fast Company reporter Victor Dey about his vision for a faster and cheaper AI grid that could serve as an alternative to large data centers, and more effectively route inference workloads. “Many inference tasks do not need the largest model or the largest cluster or the most expensive compute,” says Leighton. “What they need is the ability to marry the right model, in the right place, data and moment, at the most effective cost.” 

MIT researchers have developed a new method for precisely moving columns of individual atoms within a material, which could give rise to exotic quantum properties and shed light on quantum behavior, reports Tim Wogan for Physics World. “I’m excited because of the scalability of this that allows us to look at the interactions between the defects rather than just creating a defect itself,” explains Prof. Frances Ross. 

A new tool developed by MIT researchers could help violin designers test how an instrument might sound when certain dimensions or properties are changed without even pulling out a bow, reports Jennifer Ouellette for Ars Technica. The researchers crafted a virtual violin, “a computer simulation tool that can capture the precise physics of the instrument and even reproduce a realistic sound of a plucked string,” Ouellette explains. 

Researchers from MIT have created a new model that can predict wave behavior on different planets, reports Kristen French for Nautilus. “On Earth, waves form as wind drags across bodies of water, pushing unevenly on their surfaces. As the waves lengthen, and the distance between crests grows, the waves are increasingly driven by the force of gravity rather than by surface tension,” French writes. “On faraway planets, the size of the waves would depend not only on the strength of gravity and the speed and direction of the wind, but the density of the atmosphere, the viscosity of the liquid in the oceans and lakes, as well as the depth of the bed. All these factors were fed into the PlanetWaves model.” 

MIT scientists have developed a new model, dubbed "PlanetWaves," that predicts wave behavior on different planets, showing that the "smallest gust of wind on Titan could generate huge, roiling waves across seas of hydrocarbons," reports Andrew Paul for Popular Science. “PlanetWaves is far more than a novel simulator,” writes Paul. “Calculating fluid behaviors on distant planets and moons could help inform engineers building new spacecraft and probes.”

MIT researchers have developed a new traffic navigation system that more accurately reflects travel time by including parking data, reports Catherine Parotta for Boston 25. “What we can do is figure out if you’re best off trying this parking lot first, even if it’s farther than the closest parking lot,” explains Prof. Cathy Wu. Graduate student Cameron Hickert adds that: “We hope that this can help people make better decisions." 

Researchers at MIT have “found a way to transform a flat sheet into a functional 3D object with a single pull of a string,” reports Luis Prada for Vice. “The team developed a computational method that lets users design three-dimensional objects that can be fabricated as flat grids and then deployed almost instantly with a single tug,” explains Prada. 

Researchers at MIT have developed a new type of material that can transform into a 3D structure with the simple pull of a string, reports Gayoung Lee for Gizmodo. The new material could “have an impressive range of applications, from transportable medical devices and foldable robots to modular space habitats on Mars,” Lee explains. 

Writing for Meteorological Technology International, Alex Pack explores how MIT researchers have developed a new “lightning-prediction model that could help protect more unconventional aircraft designs – such as blended-wing bodies or truss-braced configurations – as aviation moves beyond traditional tube-and-wing designs.” 

Researchers at MIT have developed a new method that can predict how plasma will behave in a tokamak reactor given a set of initial conditions, reports Gayoung Lee for Gizmodo. The findings “may have lowered one of the major barriers to achieving large-scale nuclear fusion,” explains Lee. 

Prof. Caitlin Mueller has been named Innovator of the Year by Architectural Record for her work advancing a “vision for building design and construction that unites these disciplines with computation to create structure that are sustainable, high performing, and delightful,” reports Architectural Record. “Her group develops computational design and digital fabrication methods that integrate efficiency, performance, material circularity, and architectural expression,” Architectural Record notes. “This work spans robotic assembly of optimized trusses, fabrication of low-cost earthen and concrete systems, and algorithmic strategies for reusing salvaged wood and reassembled concrete parts.”