2025-10-08 23:32:00
Physics simulations have a problem—engineers who need those simulations’ results often don’t have time to wait. Add in real-world settings with multiple independent calculations required (for example, separate thermal, mechanical, and electromagnetic elements in the system), and “multiphysics” computations that are both realistic and real-time might seem an either-or proposition.
Coders and modelers gathering in Burlington, Mass. this week will be exploring new inroads to multiphysics on-the-go in the COMSOL simulation software environment. Over three days of keynotes, workshops, and demos, COMSOL users will be weighing new approaches to resolving the simulation time crunch.
“Surrogate models are an interesting new technology where you take your fully-fledged multiphysics model and compress it down into a compact format that’s quick to evaluate using machine learning,” says Bjorn Sjodin, senior vice president of product management at the Stockholm-based parent company, also called COMSOL.
The challenge is more widespread than COMSOL alone, too. According to a review published earlier this year in the journal Procedia Computer Science, a range of industries face simulation bottlenecks where, the authors say, “executing high-fidelity simulation can take even weeks per design.”
Surrogate models, the Procedia authors note, involve whittling equations down to simplified versions of the larger simulation environments. In other words, the surrogates capture essential behaviors of specific systems being modeled but without so much computational overhead. Often this trimming-down process can involve strategically sampling the original complex model at key points, and then training a faster approximation that can predict results for new scenarios.
“You can evaluate these models instantaneously,” Sjodin says of COMSOL’s surrogate modeling system. “Whereas if you solve the full model with unknown inputs, it could take you 15 minutes. And people are very impatient.”
According to Sjodin, European automotive manufacturers are now using COMSOL’s surrogate models to rapidly simulate entire electric vehicle battery packs, enabling real-time decisions that managers and engineers had once needed to wait a coffee break or longer for. Meanwhile, Sjodin adds, a Swiss institute has deployed the COMSOL surrogate system as an app for Indian farmers to predict food spoilage in cold storage. The surrogate simulation, the institute found, enabled the farmers to reduce food spoilage by 20 percent.
COMSOL’s full numerical simulations predict performance of an antenna surface (right sphere), while its streamlined surrogate model (left sphere) arrives at nearly the same results in substantially less running time.COMSOL
Sjodin says COMSOL intends to turn users of the simulation system into something closer to software developers in their own right.
“You can compile those apps into standalone executables that you can distribute around the world without any kind of license payment,” Sjodin says.
The company’s surrogate models, he says, are able to run as standalone applications, which can work on laptops or smartphones.
“If you want to give this to someone on the factory floor, these surrogate models are really useful because it allows you to evaluate and get results immediately,” Sjodin says. The models run quickly compared to the full multiphysics simulation because the app version of, say, a specific battery pack’s thermal performance and chemical composition comes pre-loaded. The simulation is fast, because it already has on hand pre-calculated parameters specific to the physical environment to be simulated—and only the environment to be simulated.
In addition to AI smarts that speed up the computing time for each run, COMSOL relies on other tricks as well. What modelers call “reduced order” models (ROMs) involve optimizations like mathematical pattern recognition and slimming down some of the more complicated equations in a calculation. “Neural networks come into play there, but also other technologies, more traditional reduced order modeling technologies,” he says.
For instance, in a 2024 industry-wide review of ROMs, researchers from Trieste, Italy’s International School for Advanced Studies described a range of ROM techniques that are based on more than just AI or neural networks.
“ROMs are divided in two big families: intrusive methods, in which one manipulates directly the governing equations, and non-intrusive methods, in which only the simulation data are considered,” the researchers wrote. The paper shows that a mixture of neural nets and more conventional mathematical ROM tools can achieve computational speedups up to 100,000 times as fast as models without ROM smarts added in.
2025-10-08 21:46:46
While most of the AI world is racing to build ever-bigger language models like OpenAI’s GPT-5 and Anthropic’s Claude Sonnet 4.5, the Israeli AI startup AI21 is taking a different path.
AI21 has just unveiled Jamba Reasoning 3B, a 3-billion-parameter model. This compact, open-source model can handle massive context windows of 250,000 tokens (meaning that it can “remember” and reason over much more text than typical language models) and can run at high speed, even on consumer devices. The launch highlights a growing shift: smaller, more efficient models could shape the future of AI just as much as raw scale.
“We believe in a more decentralized future for AI—one where not everything runs in massive data centers,” says Ori Goshen, Co-CEO of AI21, in an interview with IEEE Spectrum. “Large models will still play a role, but small, powerful models running on devices will have a significant impact” on both the future and the economics of AI, he says. Jamba is built for developers who want to create edge-AI applications and specialized systems that run efficiently on-device.
AI21’s Jamba Reasoning 3B is designed to handle long sequences of text and challenging tasks like math, coding, and logical reasoning—all while running with impressive speed on everyday devices like laptops and mobile phones. Jamba Reasoning 3B can also work in a hybrid setup: simple jobs are handled locally by the device, while heavier problems get sent to powerful cloud servers. According to AI21, this smarter routing could dramatically cut AI infrastructure costs for certain workloads—potentially by an order of magnitude.
With 3 billion parameters, Jamba Reasoning 3B is tiny by today’s AI standards. Models like GPT-5 or Claude run well past 100 billion parameters, and even smaller models, such as Llama 3 (8B) or Mistral (7B), are more than twice the size of AI21’s model, Goshen notes.
That compact size makes it more remarkable that AI21’s model can handle a context window of 250,000 tokens on consumer devices. Some proprietary models, like GPT-5, offer even longer context windows, but Jamba sets a new high-water mark among open-source models. The previous open-model record of 128,000 tokens was held by Meta’s Llama 3.2 (3B), Microsoft’s Phi-4 Mini, and DeepSeek R1, which are all much larger models. Jamba Reasoning 3B can process more than 17 tokens per second even when working at full capacity—that is, with extremely long inputs that use its full 250,000-token context window. Many other models slow down or struggle once their input length exceeds 100,000 tokens.
Goshen explains that the model is built on an architecture called Jamba, which combines two types of neural network designs: transformer layers, familiar from other large language models, and Mamba layers, which are designed to be more memory-efficient. This hybrid design enables the model to handle long documents, large codebases, and other extensive inputs directly on a laptop or phone—using about one-tenth the memory of traditional transformers. Goshen says the model runs much faster than traditional transformers because it relies less on a memory component called the KV cache, which can slow down processing as inputs get longer.
The model’s hybrid architecture gives it an advantage in both speed and memory efficiency, even with very long inputs, confirms a software engineer who works in the LLM industry. The engineer requested anonymity because they’re not authorized to comment on other companies’ models. As more users run generative AI locally on laptops, models need to handle long context lengths quickly without consuming too much memory. At 3 billion parameters, Jamba meets these requirements, says the engineer, making it a model that’s optimized for on-device use.
Jamba Reasoning 3B is open source under the permissive Apache 2.0 license and available on popular platforms such as Hugging Face and LM Studio. The release also comes with instructions for fine-tuning the model through an open-source reinforcement-learning platform (called VERL), making it easier and more affordable for developers to adapt the model for their own tasks.
“Jamba Reasoning 3B marks the beginning of a family of small, efficient reasoning models,” Goshen said. “Scaling down enables decentralization, personalization, and cost efficiency. Instead of relying on expensive GPUs in data centers, individuals and enterprises can run their own models on devices. That unlocks new economics and broader accessibility.”
2025-10-08 02:00:03
When you’re buying a new item of clothing, you probably don’t give much thought to the design and assembly processes the garment went through before arriving at the store.
Creating a piece of apparel starts with a designer sketching out an idea. Then a pattern is made, the fabric is chosen and cut, and the garment is sewed. Finally the clothing is packaged and shipped.
To expedite the process, some apparel companies now use 3D technologies including design software, body scans, visualization, and 3D printers. The tools allow designers to envision their creations in a variety of colors, fabrics, and motifs. Avatars known as digital twins are created to simulate how the clothes will look and fit on different body types. Body scans generate measurements for better-fitting clothing and improved product design.
Some manufacturers incorporate artificial intelligence to streamline operations, and additional companies likely will explore it as it becomes more accurate.
Not all garment makers are utilizing 3D technologies to their fullest potential, however.
To advance 3D technology for designers, manufacturers, and retailers, the 3D Retail Coalition holds an annual challenge that spotlights academic institutions and startups that are leading the way. The contest is cosponsored by the IEEE Standards Association Industry Connections 3D Body Processing program, which works with the clothing industry to create standards for technology that uses 3D scans to create digital models.
The winners of this year’s contest were selected in June at the PI Apparel Fashion Tech Show, held in New York City.
The Fashion Institute of Technology (FIT) placed first in the academic category. The New York City school offers programs in design, fashion, art, communications, and business.
PixaScale won the startup category. Based in Herzogenaurach, Germany, the consultancy assists fashion and consumer goods companies with automating content, managing 3D digital assets, and improving workflows.
Ill-fitting garments, shoes, and accessories are problems for clothing companies. The average return rate worldwide for clothing ordered online is more than 25 percent, according to PrimeAI.
To make ready-to-wear clothing, designers use grading, a process that takes an initial sample pattern of a base size using established standards and 3D body scans, then makes smaller and larger versions to be mass-produced. But the resulting clothes do not fit everyone.
Returns, which can be frustrating for shoppers, are costly for clothing companies due to reshipping and restocking expenses.
Some customers can’t be bothered to send back unwanted items, and they throw them in the garbage, where they end up in landfills.
“What if we could go back to the days when you would go to a shop, get measured, and someone would custom-make your garment?” posits Leigh LaVange, an assistant professor of technical design and patternmaking at FIT.
That was the idea behind LaVange’s winning project, Automated Custom Sizing. Her proposal uses 3D technology and AI to produce custom-tailored clothing on demand for all body types. She outlined short- and long-term scalable solutions in her submission.
“I want to fix our fit problem, but I also realize we can’t do that as an industry without changing the manufacturing process.” —Leigh LaVange
“I see it [custom sizing] as a solution that can be automated and eventually rolled out across all different types of brands,” she says.
The short-term proposal involves measuring a person’s base body specifications, such as bust, waist, thighs, biceps, and hips—either manually or from a 3D body scan. An avatar of the customer is then created and entered into a database preloaded with 3D representations of various sizes of the sample garment. The AI program notes the customer’s specs and the existing sizes to determine the best fit. If, for example, the person’s chest matches the medium-size dimensions but the hips are a few millimeters larger, the program still might recommend medium because it determined the material around the hips had enough excess fabric. A rendering of an avatar wearing an item is shown to customers to help them decide whether to make the purchase.
LaVange says her solution will help improve customer satisfaction and minimize returns.
Her long-term plan is a truly customized fit. Using 3D body scans, an AI program would determine the necessary adjustments to the pattern based on the customer’s specifications and critical fit points, like the waist, while preserving the original design. The 3D system then would make alterations, which would be rendered on the customer’s avatar for approval. The solution would eliminate excess inventory, LaVange says, because the clothing would be custom-made.
Because her proposals rely on technologies not currently used by the industry and a different way of interacting with customers, a shift in production would be required, she says.
“Most manufacturing systems today are set up to produce as many units as possible in a single day,” she says. “I believe there’s a way to produce garments efficiently if you set up your manufacturing facility correctly. I want to fix our fit problem, but I also realize we can’t do that as an industry without changing the manufacturing process.”
The winning submission in the startup category, AI-First DAM [digital asset management] as an Intelligent Backbone for Agile Product Development, uses 3D technology and AI to combine components of clothing design into a centralized platform.
Kristian Sons, chief executive of Pixascale, launched the startup in February. He left Adidas in January after nine years at the company, where he was the technical lead for digital creation.
Many apparel companies, Sons says, still store their 3D files on employees’ local drives or on Microsoft’s SharePoint, a Web-based document-management system.
Those methods make things difficult because not everyone has access.
Sons’ cloud-based platform addresses the issue by sharing digital assets, such as images, videos, 3D models, base styles, and documents, to all parties involved in the process.
That includes designers, seamstresses, and manufacturers. His system integrates with the client’s file management system, providing access to the most recent images, renderings, and other relevant data.
His DAM system also includes a library of embellishments such as zippers and buttons, as well as fabric options.
“Getting this information into a platform that everyone can easily access and can understand what others did really builds a foundation for collaboration.” —Kristian Sons
“Getting this information into a platform that everyone can easily access and track what others did really builds a foundation for collaboration,” he says.
Sons also is working on incorporating AI agents and large language models to connect with internal systems and application programming interfaces to autonomously conduct simple research requests.
That might include suggesting new products or different silhouettes, or modifying the previous season’s offerings with new colors, Sons says.
“These AI agents certainly will not be perfect, but they are a good starting point so designers don’t have to start from scratch,” he says. “I think using AI agents is super exciting because in the past few years in the fashion industry, we have been talking about how AI would do the creative parts, like designing a product. But now we’re talking about the AI doing the low-level tasks.”
A demonstration of how Pixascale’s DAM works is on YouTube.
2025-10-07 22:00:03
Aeva Technologies, a developer of lidar systems based in Mountain View, Calif., has unveiled the Aeva Eve 1V, a high-precision, noncontact motion sensor built on its frequency modulated continuous wave (FMCW) sensing technology. The company says that the Eve 1V measures an object’s motion with accuracy, repeatability, and reliability—all without ever making contact with the material. That last point is key for the Eve 1V’s intended environment: Industrial manufacturing.
Today’s manufacturing lines are under pressure to deliver faster production, tighter tolerances, and zero defects, often while working with a wide variety of delicate materials. Traditional tactile tools such as measuring wheels and encoders can slip, wear out, and cause costly downtime. Many noncontact alternatives, while promising, are either too expensive or fall short in accuracy and reliability under real-world conditions, says Mina Rezk, cofounder and chief technology officer at Aeva.
“Eve 1V was built to solve that exact gap: A compact, eye-safe, noncontact motion sensor that delivers submillimeter-per-second velocity accuracy without touching the material, so manufacturers can eliminate slippage errors, avoid material damage, and reduce maintenance-related downtime, enabling higher yield and more predictable operations,” Rezk says.
Unlike traditional lidar that sends bursts of light and waits for those bursts to return to make measurements, FMCW continuously emits a low-power laser while sweeping its frequency. By comparing outgoing and returning signals, it detects frequency shifts that reveal both distance and velocity in real time. The additional measurement of an object’s velocity to its position in three-dimensional space makes FMCW a type of 4D lidar.
Eve 1V is the second member of its Eve 1 family, following the launch of the Eve 1D earlier this year. The Eve 1D is a compact displacement sensor capable of detecting movement at the micrometer scale, roughly 1/100 the thickness of a human hair. “Together, Eve 1D and Eve 1V show how we can take the same FMCW perception platform and tailor it for different industrial needs: Eve 1D for distance measurement and vibration detection, and Eve 1V for precise velocity and length measurement,” Rezk says.
Future applications could extend into robotics, logistics, and consumer health, where noncontact sensing may enable the detection of microvibrations on human skin for accurate pulse and blood-pressure readings.
The company’s core FMCW architecture, originally developed for long-range 4D lidar for automobiles, can be adjusted through software and optics for highly precise motion sensing at close range in manufacturing, according to Rezk. This flexibility means the system can track extremely slow movements, down to fractions of a millimeter per second, in a factory setting, or it can monitor faster motion over longer distances in other applications.
By avoiding physical contact, Eve 1V eliminates wear and tear, slippage, contamination, or the need for physical access to the part. “That delivers three practical advantages in a factory: One, maintenance-free operation with no measuring wheels to replace or recalibrate; two, material friendliness—you can measure delicate, soft, or textured surfaces without risk of damage, and three, operational robustness—no slippage errors and fewer stoppages for service,” Rezk says. Put together, that means more uptime, steady throughput, and less scrap, he adds.
When measuring velocity, engineers often rely on one of three tools: encoders, laser velocimeters, or camera-based systems. Each has its strengths and its drawbacks. Traditional encoders are low-cost but can wear down over time. Laser-based velocity-measurement systems, while precise, tend to be large and expensive, making them difficult to implement widely. And camera-based approaches can work for certain inspection tasks, but they usually require markers, controlled lighting, and complex processing to measure speed accurately.
Rezk says that the Eve 1V system offers a balance of these options. It provides precise and consistent velocity measurements without contacting material, making it compact, safe, and simple to install. Its outputs are comparable with existing encoder systems, and because it doesn’t rely on physical contact, it requires minimal maintenance.
This approach helps cut down on wasted energy from slippage, eliminates the need for maintenance tied to parts that wear out, and ultimately lowers long-term operating costs—especially when compared with traditional contact-based systems or expensive laser options.
This method avoids stitching together frame-by-frame comparisons and resists interference from sunlight, reflections, or ambient light. Built on silicon photonics, it scales from micrometer-level sensing to millimeter-level precision over longer ranges. The result is clean, repeatable data with minimal noise—outperforming legacy lidar and camera-based systems.
Aeva is expecting to begin full production of the Eve 1V in early 2026. The Eve 1V reveal follows a recent partnership with LG Innotek, a components subsidiary of South Korea’s LG Group, under which Aeva will supply its Atlas Ultra 4D lidar for automobiles, with plans to expand the technology into consumer electronics, robotics, and industrial automation.
2025-10-07 21:00:00
Happy IEEE Day!
First celebrated in 2009, IEEE Day commemorates the initial gathering of IEEE members to share their technical ideas in 1884.
Worldwide celebrations demonstrate the ways thousands of IEEE members in local communities join together to collaborate on ideas that leverage technology for a better tomorrow.
“IEEE Day 2025 is a celebration of innovation, collaboration, and the incredible impact our members create worldwide,” says Abdul Halik M I, this year’s IEEE Day chair. “I encourage everyone to join in, share their stories, and be part of this global movement.”
Celebrate IEEE Day with colleagues from IEEE Sections, Student Branches, Affinity groups, and Society Chapters. Events happen both virtually and in person all around the world.
Every year, IEEE members from IEEE Sections, Student Branches, Affinity groups, and Society Chapters join hands to celebrate IEEE Day. Events happen both virtually and in person. IEEE Day celebrates the first time in history when engineers worldwide gathered to share their technical ideas in 1884.
Check out our special offers and activities for IEEE members and future members. And share these with your friends and colleagues.
Have some fun and compete in the photo and video contests. Get your phone and camera ready when you attend one of the events. This year we will have both Photo and Video Contests. You can submit your entries in STEM, technical, humanitarian and social categories.
2025-10-07 01:56:02
Natron Energy, a Santa Clara, California-based sodium-ion battery startup, ceased operation on 3 September due to funding issues. Just a year ago, the company made headlines for its plans to build a first-of-its-kind US $1.4 billion factory in North Carolina to manufacture up to 14 gigawatt-hours of sodium-ion batteries. While experts say Natron’s closure shouldn’t be taken as a harbinger for the rest of the emerging industry in the United States, they acknowledge that the West is behind China, which is leveraging its dominance in lithium-ion batteries to forge ahead on sodium-ion battery manufacturing.
In the U.S., sodium-ion startups like Natron, which launched in 2012, tend to rely on goodwill from funders, says K.M. Abraham, a retired research professor at Northeastern University in Boston and CTO of lithium-ion battery consulting firm E-KEM Sciences. This can pose challenges for companies when funding timelines outpace innovations.
“Companies aren’t able to make progress quickly enough to keep up with pressure exerted by the investors,” he says.
Until recently, Natron was seen as a leader of the pack in the U.S. sodium-ion market. Part of the company’s appeal was its pioneering approach to low-cost electrodes, the conductors at the battery’s positive and negative terminals, which make contact with the non-metallic part of the circuit. The company used Prussian Blue, a pigment found in paints and dyes, to make both the cathode and anode for its three battery systems. In addition to having a low material cost, Prussian Blue’s chemical structure has large pores, helping it facilitate faster ion transfer between the electrodes.
Natron was the first in the world to commercialize a sodium-ion battery using Prussian Blue, a real feat considering China’s battery manufacturing might, says Tyler Evans, co-founder and CEO of Mana Battery, a Broomfield, Colorado-based sodium-ion battery cell startup that launched in 2023.
“They were doing it in the West, and they were scaling a technology that was relatively low energy density for a very specific market segment,” says Evans about Natron’s products.
Mana is another U.S. startup focusing on bringing sodium-ion batteries to market.Nicholas Singstock/Mana
That market included grid storage, data center power backups, and electric vehicle charging stations—large-scale stationary applications where attributes like safety and cost rank higher than energy density. Natron’s success in this space, including its plans for the North Carolina factory, prompted questions about whether sodium-ion could emerge as a direct replacement for lithium-ion batteries. United Airlines and Chevron were on the list of Natron’s investors.
But Evans says scaling up a low-energy density product while building out manufacturing lines is expensive. “If you think about building a manufacturing facility where you want to produce 10 gigawatt hours of batteries, if your energy density is very low, producing an equivalent number of batteries requires more manufacturing lines,” Evans says.
“If you think about building a manufacturing facility where you want to produce a gigawatt-hour of battery manufacturing capacity, if your energy density per battery cell is very low, producing that capacity requires more manufacturing lines,” Evans says, meaning significantly more capital and operational expenditure in an already capital-intensive undertaking.
In 2023, Natron’s systems made it to market. The company partnered with Encorp to deploy the industry’s first multi-megawatt class power platform for industrial applications. A year later, in 2024, Natron opened the U.S.’s first commercial scale manufacturing facility in Holland, Michigan to supply data centers with energy storage. The U.S. Department of Energy’s ARPA-E program provided $19.8 million to Natron as part of a $300 million facility upgrade to transition from lithium-ion battery manufacturing to sodium-ion battery manufacturing. That facility shut its doors at the same time as Natron’s California headquarters on 3 September.
A request for comment from Natron resulted in an automated message to contact the company’s primary shareholder, Sherwood Partners. Sherwood Partners did not respond to a request for comment.
Adrian Yao is the founder and team lead of Stanford’s STEER initiative, a DOE-funded research program. He’s also an author of a January 2025 paper assessing how sodium-ion batteries measure up to lithium-ion batteries in terms of technology and cost.
While he was impressed with Natron’s technology and product, he says that the company may have been ahead of the curve on the data center market niche it had carved out for itself. “Hyperscalers right now, their primary concern is just getting connected and building data centers,” says Yao. “I think timing on that cycle may be early, and it’s unfortunate things don’t always work out.”
Natron joins Stanford spin-out Bedrock Materials as the second sodium-ion company to fold this year. Bedrock cited market and innovation challenges for its April closure.
“The battery business is very difficult. There are a lot of tombstones,” says Andrew Thomas, president and cofounder of Acculon Energy, a Columbus, Ohio-based startup marketing two battery modules with sodium-ion cells for industrial energy and EVs that travel at low speeds, like golf carts. Unlike Natron, Acculon, which launched in 2022, employs more traditional layered-metal oxides and other sodium chemistries.
Thomas says it’s this distinction that makes it hard to draw conclusions about the U.S. sodium-ion battery industry as a whole in light of Natron’s closure. Comparing different sodium-ion chemistries, like Prussian Blue or layered metal oxides, is like comparing apples to oranges.
“I don’t think one failure is representative of a country being unable, but we’re at a significant disadvantage given the installed base in China,” Thomas says.
China is the dominant player in sodium-ion battery development, with companies like CATL displaying their designs at tech expos.Yuan Zheng/VCG/AP
China has long dominated the battery industry, and sodium-ion batteries are no exception. Today, China produces more than 75 percent of batteries sold globally, according to the International Energy Agency. On the sodium-ion front, developers like CATL have moved into second-generation batteries, with the April launch of Naxtra, a brand geared toward EV applications.
Yao says he’d like to see the U.S. concentrate its focus more on building up its manufacturing prowess to compete with China. “My broader critique of the Western Hemisphere in terms of our thinking and obsession with trying to innovate ourselves out of the problem, is that we focus too much on tech,” Yao says. “We have very little manufacturing experience… Our yield rates are abysmal, and our workforce is not trained.”
Founders like Evans and Thomas are optimistic about their prospects as growing demand for grid storage, data centers, and low-cost mobility applications drives the need for applications they say sodium-ion batteries are uniquely equipped to support in terms of temperature range, safety, and cost metrics. When it comes to manufacturing, Mana is taking a page from China’s playbook by partnering with existing manufacturers to scale up production.
Evans says there’s an appetite for this kind of partnership in the U.S. right now. “I think it’s a commercialization sweet spot that’s specific to sodium.”