It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Sunday, November 23, 2025
Uniform reference system for lightweight construction methods
Comparison of mechanical and geometric properties for industry and development
Lightweight components are generally designed with computer-based methods before being manufactured. There are various common methodologies. Because they use different physical and mathematical descriptions, however, direct comparisons are difficult. Moreover, the highly complex computation methods limit them to low spatial resolutions. With their Stress-Guided Lightweight 3D Designs (SGLDBench) benchmark, the researchers have succeeded in overcoming these serious obstacles.
SGLDBench standardizes lightweight design methods
SGLDBench permits six reference strategies such as classical topology optimization, porous infill structures or lattice-based layouts to be applied to arbitrary components with user-defined boundary conditions and compared using 3D simulations. It incorporates such parameters as stiffness-to-weight, stress fields and deformability as well as information on how the component or structure is connected to or positioned in its surroundings. This enables users to create designs with different resolutions and material use while evaluating the mechanical and geometric characteristics.
The new benchmark has potential applications in many areas: for example, it enables testing of various design variants for hip implants followed by customized manufacturing. In the automotive and aerospace industries, the benchmark will also help to make parts even leaner. In those areas, weight savings lead to improved energy efficiency. At the same time, the structures must be designed to meet stringent safety standards in their ability to withstand shocks and vibrations.
Benchmark allows more than 100 million simulation elements
“With SGLDBench we have created a transparent benchmark for lightweight design,” says Rüdiger Westermann, Professor for Computer Graphics and Visualization at the TUM School of Computation, Information and Technology. “That will not only help researchers with the classification of methods, but will also give companies a tool for reaching well-founded decisions in product development.” At present, SGLDBench can perform simulations with more than 100 million elements on an affordable desktop computer in much faster times than commercial products.”
Among the methods making this possible, the researchers used new approaches in particular for the efficient solution of large systems of equations for stress simulations and optimized them for conventional computer architectures.
The digital twin developed by this year’s winning team enables real-time, data-driven tsunami forecasting with dynamic adaptivity to complex source behavior.
Existing state-of-the art high-performance computing simulations for early tsunami warning are developed primarily through models which process seismic data. The drawbacks of these approaches include: 1) They do not allow for enough warning time, as destructive tsunami waves can arrive onshore in under ten minutes, and 2) They fail to capture the complexities of earthquake ruptures which cause the Tsunamis.
The Gordon Bell Prize-winning team created a far more predictive early warning framework by developing a full-physics Bayesian inversion framework—popularly called “digital twin.” A digital twin is a virtual simulation of a physical process (or object) that uses real-time data from sensors to match its physical counterpart. The digital twin developed by this year’s winning team enables real-time, data-driven tsunami forecasting with dynamic adaptivity to complex source behavior.
With this approach, they achieved the fastest time-to-solution of a partial differential equation (PDE)-based Bayesian inverse problem with 1 billion parameters in 0.2 seconds, a ten-billion-fold speedup over the existing state-of-the-art. This is the largest-to-date unstructured mesh finite element (FE) simulation with 55.5 trillion degrees of freedom (DOF) on 43,520 GPUs, with 92% weak and 79% strong parallel efficiencies in scaling over a 128× increase of GPUs on the full-scale El Capitan system—the world’s largest supercomputer.
The team simulated a Tsunami in an area in the Pacific Ocean called the Cascadia Subduction Zone, which stretches 1000 km from northern California to British Columbia. This area has been eerily quiet for over 300 years—but is considered overdue for a magnitude 8.0–9.0 megathrust earthquake.
The members of the ACM Gordon Bell Prize-Winning team are Stefan Henneking, Sreeram Venkat, Milinda Fernando, and Omar Ghattas (all of The University of Texas at Austin); Veselin Dobrev, John Camier, Tzanio Kolev (all of Lawrence Livermore National Laboratory); and Alice-Agnes Gabriel (University of California San Diego).
Honorable Mention This year an Honorable Mention for the ACM Gordon Bell Prize was given to a 10-member team from ETH Zurich for their project “Ab-initio Quantum Transport with the GW Approximation, 42,240 Atoms, and Sustained Exascale Performance.” Team members include Nicolas Vetsch, Alexandros Nikolaos Ziogas, Alexander Maeder, Vincent Maillou, Anders Winka, Jiang Cao, Grzegorz Kwasniewski, Leonard Deutschle (also affiliated with NVIDIA), Torsten Hoefler, and Mathieu Luisier.
About ACM ACM, the Association for Computing Machinery is the world’s largest educational and scientific computing society, uniting computing educators, researchers, and professionals to inspire dialogue, share resources and address the field’s challenges. ACM strengthens the computing profession’s collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.
About the ACM Gordon Bell Prize The ACM Gordon Bell Prize is awarded each year to recognize outstanding achievement in high-performance computing. The purpose of this recognition is to track the progress over time of parallel computing, with particular emphasis on rewarding innovation in applying high-performance computing to applications in science. The prize is awarded for peak performance as well as special achievements in scalability and time-to-solution on important science and engineering problems and low price/performance. Financial support for the $10,000 awards is provided by Gordon Bell, a pioneer in high-performance and parallel computing.
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First-ever full Earth system simulation provides monumental new tool to understand climate change
Breakthrough in climate modelling integrates weather and climate processes with far greater precision than previous frameworks
The Gordon Bell Climate Prize-winning team reached a landmark this year by being the first team ever to develop a Full Earth Simulation at 1 km (extremely high) Resolution.
St. Louis, MO, November 20, 2025 – ACM, the Association for Computing Machinery, today presented a 26-member team with the ACM Gordon Bell Prize for Climate Modelling in recognition of their project “Computing the Full Earth System at 1 km Resolution.” The award honors innovative contributions to parallel computing toward solving the global climate crisis.
Climate change is responsible for more extreme hurricanes, more destructive wildfires, severe droughts, and increased human disease, among other harmful outcomes. Experts warn that if carbon emissions are not significantly reduced within a few decades, the damage to the Earth’s ecosystem will be irreversible.
Among the most effective tools scientists have developed to understand climate change are digital simulations of the Earth. These simulations are produced by developing specific algorithms to run on the world’s most powerful supercomputers. But simulating how human activity influences the climate has been an extraordinarily difficult challenge. A mind-boggling number of variables need to be taken into consideration—such as the cycles of water, energy, and carbon, how those factors relate to each other, and how diverse physical, biological, and chemical processes interact over space and time. For these reasons, previous state-of-the-art simulations have not been able to achieve what is referred to as a “Full Earth System” simulation.
The Gordon Bell Climate Prize-winning team reached a landmark this year by being the first team ever to develop a Full Earth Simulation at 1 km (extremely high) Resolution. In their introduction, they explain, “We present the first-ever global simulation of the full Earth system at 1.25 km grid spacing, achieving highest time compression with an unseen number of degrees of freedom. Our model captures the flow of energy, water, and carbon through key components of the Earth system: atmosphere, ocean, and land. To achieve this landmark simulation, the team harnessed the power of 8192 GPUs on Alps and 4096 GPUs on JUPITER, two of the world’s largest GH200 superchip installations.”
The groundbreaking innovations the team employed to make the Full Earth Simulation possible include (1) exploiting functional parallelism by efficiently mapping components to specialized heterogeneous systems and (2) simplifying the implementation and optimization of an important component by separating its implementation in Fortran from the optimization details of the target architecture.
In the conclusion to their paper they write, “This has enormous and enduring potential to provide full global Earth system information on local scales about the implications of future warming for both people and eco-systems, information that otherwise would not exist.”
Team Members The members of the team are: Alexandru Calotoiu, Torsten Hoefler, Yakup Budanaz, Pratyai Mazumder, Marcin Copik, and Benjamin Weber (all of ETH Zurich); Jan Frederik Engels, Hendryk Bockelmann, and Claudia Frauen (all of Deutsches Klimarechenzentrum), Dmitry Alexeev (NVIDIA), Daniel Klocke, René Redler, Reiner Schnur, Helmuth Haak, Luis Kornblueh, Cathy Hohenegger, and Bjorn Stevens (all of the Max Planck Institute for Meteorology); Fatemeh Chegini (University of Hamburg), Manoel Römmer, Lars Hoffmann, Sabine Griessbach, Mathis Bode, and Andreas Herten (all of Forschungszentrum Jülich); as well as Jonathan Coles, Miguel Gila, and William Sawyer (all of the Swiss National Supercomputing Centre).
About ACM ACM, the Association for Computing Machinery is the world’s largest educational and scientific computing society, uniting computing educators, researchers, and professionals to inspire dialogue, share resources, and address the field’s challenges. ACM strengthens the computing profession’s collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.
About the ACM Gordon Bell Prize for Climate Modeling The ACM Gordon Bell Prize for Climate Modelling recognizes innovative parallel computing contributions toward solving the global climate crisis. Climate scientists and software engineers are evaluated for the award based on the performance and innovation in their computational methods. A cash prize in the amount of $10,000 accompanies the award, which was conceived and funded by Gordon Bell, a pioneer in high performance computing and researcher emeritus at Microsoft Research. Recipients of the ACM Gordon Bell Prize for Climate Modelling will have their research published in The International Journal of High Performance Computing Applications (IJHPCA).
Scientists uncover how COVID-19 variants outsmart the immune system
Researchers at the Icahn School of Medicine and collaborators provide a roadmap for designing next-generation antibodies that might keep pace with viral evolution
The Mount Sinai Hospital / Mount Sinai School of Medicine
Above: Cartoon model of the SARS-CoV-2 spike protein showing the different regions (or domains) that are associated with known viral mutations and antibodies can recognize and attach to. These include areas involved in binding to human cells and in helping the virus fuse with them.
New York, NY [November 21, 2025]—Researchers at the Icahn School of Medicine at Mount Sinai and collaborators have created the most comprehensive map to date showing how antibodies attach to the SARS-CoV-2 virus, which causes COVID-19, and how viral mutations weaken that attachment. The findings, published in the November 21 online issue of Cell Systems, a Cell Press journal, explain why variants like Omicron can evade immune defenses and suggest new strategies for building longer-lasting antibody therapies and vaccines.
The team analyzed more than a thousand three-dimensional structures of antibodies bound to the virus’s spike protein, the main target for immune recognition, and compiled them into a structural atlas of COVID-19 antibodies. By studying these structures together for the first time, the researchers revealed a detailed picture of how the immune system targets the virus and how the virus evolves to evade it.
“Scientists around the world have solved thousands of individual antibody-virus structures, but until now, no one had looked at them together,” says senior author Yi Shi, PhD, Associate Professor of Pharmacological Sciences, and Director of the Center for Protein Engineering and Therapeutics, at the Icahn School of Medicine. “By uniting all these data, we were able to see the bigger picture—how fully antibodies cover the virus’s surface and how mutations in newer variants like Omicron can undermine that protection. It gives us a clearer view of both the strengths and limits of our immune system.”
The researchers found that antibodies, including many used in clinical treatments, recognize nearly every exposed region of the spike protein’s receptor-binding domain, a critical region of the virus. Despite this broad coverage, mutations in newer variants have weakened the binding of almost all antibodies to some degree. Many antibodies, though different in sequence, bind to the virus in strikingly similar ways, suggesting that there are only a few effective structural ways to neutralize it. That convergence, say the investigators, helps explain why the virus can mutate around immunity so efficiently.
The study also highlights the potential of nanobodies—tiny, highly stable antibody fragments that can reach parts of the virus that standard antibodies often miss. Because they can recognize deeply buried regions of the spike protein that tend to remain unchanged as the virus evolves, nanobodies could serve as powerful starting points for developing next-generation antiviral drugs.
“Our findings highlight the limits of the antibodies we currently rely on,” Dr. Shi says. “While these antibodies have been remarkably effective, the virus keeps finding ways to escape them.”
“To stay ahead, we’ll need to design next-generation antibodies that can recognize and latch onto multiple regions of the virus at once, making it much harder for the virus to evade our defenses as it continues to evolve,” adds Frank (Zirui) Feng, the study’s first author and a master’s student in the Biomedical Data Science and AI program at Mount Sinai.
Although the study focused on one key part of the spike—the receptor-binding domain—the researchers note that similar patterns of immune escape are likely occurring elsewhere in the virus. They emphasize that the results do not mean the immune system or vaccines no longer work. Vaccination and natural immunity still provide vital protection through a wide range of immune responses, even when certain antibodies lose potency.
Next, the team plans to apply this large-scale structural approach to other viruses to uncover shared principles of antibody recognition. Ultimately, they hope these insights will guide the development of durable antibody treatments capable of withstanding viral evolution and improving preparedness for future pandemics.
“The immune system is remarkably adaptable, but the virus is clever,” says co-author Adolfo Garcia-Sastre, PhD, Irene and Dr. Arthur M. Fishberg Professor of Medicine, and Director of the Global Health and Emerging Pathogens Institute at the Icahn School of Medicine. “By analyzing how antibodies attach to the virus and where they fall short, we gain a detailed map of the virus’s vulnerabilities. This insight not only helps us understand why some antibodies stop working as the virus evolves but also guides the design of next-generation therapies that can stay one step ahead, potentially improving how we prevent and treat COVID-19 and other viral infections.”
As a part of this research, the team has created an open-access data set and interactive web tool that allows scientists to explore antibody structures in detail, providing a powerful resource to collectively accelerate research on COVID-19 and other viruses.
The paper is titled “One Thousand SARS-CoV-2 Antibody Structures Reveal Convergent Binding and Near-Universal Immune Escape.”
The study’s authors, as listed in the journal, are Zirui Feng, Zhe Sang, Yufei Xiang, Alba Escalera, Adi Weshler, Dina Schneidman- Duhovny, Adolfo García-Sastre, and Yi Shi.
This work is supported by National Institutes of Health grant R01 AI163011. This work is also partly supported by the Center for Research on Influenza Pathogenesis and Transmission, an National Institute of Allergy and Infectious Diseases (NIAID) Center of Excellence for Influenza Research and Response (contract # 75N93021C00014), and by NIAID grant U19AI135972. Research reported in this publication was supported by NIAID Award G20AI174733. See the Cell Systems paper for details on conflicts of interest.
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About the Icahn School of Medicine at Mount Sinai
The Icahn School of Medicine at Mount Sinai is internationally renowned for its outstanding research, educational, and clinical care programs. It is the sole academic partner for the seven member hospitals* of the Mount Sinai Health System, one of the largest academic health systems in the United States, providing care to New York City’s large and diverse patient population.
The Icahn School of Medicine at Mount Sinai offers highly competitive MD, PhD, MD-PhD, and master’s degree programs, with enrollment of more than 1,200 students. It has the largest graduate medical education program in the country, with more than 2,600 clinical residents and fellows training throughout the Health System. Its Graduate School of Biomedical Sciences offers 13 degree-granting programs, conducts innovative basic and translational research, and trains more than 560 postdoctoral research fellows.
Ranked 11th nationwide in National Institutes of Health (NIH) funding, the Icahn School of Medicine at Mount Sinai is among the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges. More than 4,500 scientists, educators, and clinicians work within and across dozens of academic departments and multidisciplinary institutes with an emphasis on translational research and therapeutics. Through Mount Sinai Innovation Partners (MSIP), the Health System facilitates the real-world application and commercialization of medical breakthroughs made at Mount Sinai.
* Mount Sinai Health System member hospitals: The Mount Sinai Hospital; Mount Sinai Brooklyn; Mount Sinai Morningside; Mount Sinai Queens; Mount Sinai South Nassau; Mount Sinai West; and New York Eye and Ear Infirmary of Mount Sinai
Journal
Cell Systems
Subject of Research
Not applicable
Article Title
One Thousand SARS-CoV-2 Antibody Structures Reveal Convergent Binding and Near-Universal Immune Escape
