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Supercomputers reveal dangerous stress buildup beneath Southern California

New 1,000-year earthquake simulations suggest parts of the San Andreas system are under record levels of strain

For over a century, Southern California has avoided the catastrophic earthquakes geologists long considered inevitable. However, a groundbreaking computational study suggests that the region is now under tectonic stress levels unseen in the past millennium. This discovery was made possible by advanced earthquake-cycle simulations that reconstruct 1,000 years of fault behavior.
 
A collaborative team from the University of Bern, the University of Hawaiʻi at Mānoa, Northern Arizona University, the U.S. Geological Survey, and the Scripps Institution of Oceanography developed a sophisticated four-dimensional model of the Southern San Andreas Fault System. Their findings reveal that stress has reached critical, historically high levels near Cajon Pass, a pivotal junction where the San Andreas and San Jacinto faults intersect.
 
Published in the Journal of Geophysical Research: Solid Earth, this research arrives amid rising concerns that Southern California may be nearing a significant seismic event. As the study notes, the region is essentially sitting on a fault system that has been storing energy for generations; following the massive 7.9 magnitude Fort Tejon earthquake of 1857, the southern San Andreas has remained uncharacteristically quiet.

A thousand years of earthquakes reconstructed in silicon

The study’s most significant contribution is not simply its conclusions, but the computational machinery used to reach them.
 
Researchers employed a physics-based earthquake-cycle simulator known as Maxwell, a semi-analytic Fourier-transform model capable of tracking stress evolution across hundreds of kilometers of fault networks. The model represents an elastic crust resting atop a viscoelastic mantle and calculates how tectonic loading, earthquake ruptures, and post-seismic relaxation interact through time.
 
The computational domain spans approximately 450 by 900 kilometers and incorporates 38 major fault segments extending from California’s Carrizo Plain to the Borrego Mountain region. The simulation integrates geodetic observations, fault locking depths, geological slip rates, and a detailed paleoseismic record covering the last millennium.
 
Rather than examining a single earthquake, the researchers recreated roughly 1,000 years of earthquake history, modeling dozens of large ruptures and tracking how stress accumulated and transferred between interconnected fault segments over centuries. The resulting calculations generated time-dependent stress fields at 10-year intervals, with annual-resolution simulations around major earthquake events.
 
This type of multi-century earthquake-cycle modeling would have been impossible only a few decades ago. The calculations involve repeated evaluations of three-dimensional fault dislocations, viscoelastic relaxation processes, and Coulomb stress interactions across an entire regional fault network.

Cajon Pass: California’s potential earthquake gate

At the center of the investigation lies Cajon Pass, a narrow corridor northeast of Los Angeles where two of California’s most important fault systems converge.
 
The location carries extraordinary significance because it appears capable of acting as what researchers call an “earthquake gate.” Under some stress conditions, ruptures stop at the junction. Under others, they may pass through it and cascade into neighboring faults, producing substantially larger earthquakes.
 
Historical evidence hints at both possibilities.
 
The massive 1857 Fort Tejon earthquake appears to have terminated near Cajon Pass. In contrast, an earlier 1812 earthquake may have propagated through the junction, linking multiple fault segments in a much larger interconnected rupture.
 
The new simulations suggest that stress relationships between neighboring fault segments may determine whether the gate remains closed or swings open.
 
Researchers found that when stress levels on the San Andreas and San Jacinto systems become more closely aligned, through-going ruptures become more likely. This raises the possibility that future earthquakes could involve multiple fault systems simultaneously rather than remaining confined to a single segment.
 
For Southern California’s densely populated urban corridor, that distinction matters enormously.

Stress levels reach modern extremes

The most troubling findings emerge from the model’s estimate of present-day conditions.
 
The simulations indicate that tectonic stress has accumulated steadily since the nineteenth century, producing elevated stress levels throughout the region. By the model’s 2025 endpoint, the Mojave South segment of the San Andreas carried approximately 2.8 megapascals of Coulomb stress, while the San Jacinto Bernardino segment reached roughly 3.6 megapascals. The latter exceeds any stress level modeled on that segment during the previous millennium.
 
The Mojave South segment is particularly concerning because researchers identified it as carrying the highest stress accumulation rate in the system, approximately 1.8 megapascals per century.
 
Even more striking is the historical context.
 
The study reports that current stress on the Mojave South segment is the highest observed anywhere in its modeled 1,000-year history. Meanwhile, stress levels on the San Jacinto Bernardino segment now exceed those present before any major rupture included in the simulation record.
 
Although the researchers emphasize that these values should not be interpreted as direct earthquake predictions, they nevertheless portray a fault system that has been loading for an exceptionally long period.

Why supercomputing matters

Earthquake hazards are notoriously difficult to forecast because faults do not operate independently.
 
A rupture on one fault can increase stress on neighboring segments while reducing stress elsewhere. These interactions can persist for decades or centuries. Untangling those relationships requires simulations that capture the evolution of entire fault networks rather than isolated faults.
 
The present study demonstrates how high-performance computing is transforming seismic hazard science from a largely observational discipline into a predictive modeling enterprise.
 
By assimilating paleoseismic records, geological slip rates, geodetic measurements, and rheological models into a unified computational framework, researchers can now evaluate thousands of years of fault evolution and explore scenarios that have never been directly observed.
 
The team even simulated hypothetical future rupture sequences, including scenarios where multiple fault segments fail together. These experiments revealed that a complete rupture involving all major Cajon Pass fault strands would produce the largest stress release observed in the entire modeled history.
 
Such analyses are increasingly relevant as emergency planners, infrastructure operators, and policymakers seek more sophisticated estimates of seismic risk.

The uncomfortable message

The study stops well short of forecasting an imminent earthquake. Earthquake occurrence remains fundamentally unpredictable, and the authors repeatedly caution that their results depend on model assumptions and fault parameters.
 
Yet the broader message is difficult to ignore.
 
More than 169 years have passed since the Fort Tejon earthquake ruptured the southern San Andreas. During that time, plate tectonic motion has continued relentlessly, loading the fault system year after year.
 
The simulations indicate that stress levels today rival, or in some cases exceed, those that preceded major historical ruptures.
 
For residents of Southern California, that is a sobering conclusion.
 
For the supercomputing community, however, it is also evidence of a profound shift in Earth science.
 
The most important discoveries about future earthquake hazards may no longer come solely from the ground beneath our feet, but from the massive computational systems capable of reconstructing centuries of tectonic history and revealing what the Earth’s faults have been quietly storing all along.
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Physics-trained ‘Digital Super Brain’ learns from supercomputers to accelerate discovery

Tyler O'Neal, Staff Editor LATEST

Chalmers researchers combine HPC simulations, photonics physics, and AI to create a new generation of scientific surrogate models

For decades, the world’s most powerful supercomputers have functioned as scientific laboratories, enabling researchers to explore complex phenomena ranging from climate systems and fusion plasmas to advanced materials and photonic devices. However, even these high-performance machines face a critical bottleneck: high-fidelity simulations are computationally expensive, often requiring days or weeks of processing time to evaluate a single design space.
 
To address this challenge, researchers at Chalmers University of Technology in Sweden have developed a "digital super brain." By integrating artificial intelligence with fundamental physical laws, they have created a machine-learning framework capable of replicating complex electromagnetic simulations with only a fraction of the computational effort. This innovation marks a paradigm shift in scientific computing, as supercomputers transition from merely performing simulations to training intelligent models that can explore new designs at unprecedented speeds.

Teaching AI the laws of physics

The Chalmers team’s breakthrough stems from a simple observation: most AI systems spend enormous effort learning physical relationships that scientists already understand.
 
Traditional neural networks are often treated as black boxes, requiring vast amounts of training data before they can accurately predict physical behavior. Generating that training data often requires thousands of large-scale simulations to run on HPC systems.
 
Instead of forcing the AI to learn everything from scratch, the researchers embedded physical knowledge directly into the neural network architecture.
 
Published in Laser & Photonics Reviews, the study introduces a framework that incorporates the physics of optical resonances through quasinormal mode (QNM) theory. The model learns the resonant behavior of photonic structures while automatically respecting fundamental physical principles such as energy conservation and causality.
 
By integrating known physics into the learning process, the researchers created a model that requires significantly less training data while maintaining high predictive accuracy.
 
For computational scientists, this represents an important shift. Rather than replacing physics with AI, the framework fuses the two into a single computational system.

Supercomputers become teachers

The most intriguing aspect of the work may be its relationship with high-performance computing.
 
The AI model depends on large quantities of training data generated through sophisticated electromagnetic simulations. These simulations, which solve Maxwell’s equations across complex nanostructured devices, are precisely the type of workloads that consume substantial HPC resources.
 
In effect, the supercomputer acts as a teacher.
 
Large simulation campaigns generate enormous datasets describing how light interacts with advanced photonic structures. The AI system then compresses this knowledge into a compact surrogate model capable of reproducing the behavior of those systems almost instantly.
 
This emerging workflow is rapidly becoming one of the most important trends in computational science:
  1. Run large-scale simulations on HPC systems.
  2. Generate high-fidelity physical datasets.
  3. Train physics-informed AI models.
  4. Deploy surrogate models for rapid design exploration.
The result is a powerful form of computational knowledge compression. Months of simulation effort can be distilled into an AI model that delivers answers in seconds.

Accelerating photonics research

The researchers demonstrated their framework on photonic crystal slabs and free-form dielectric metasurfaces, structures that manipulate light at the nanoscale and play important roles in sensing, communications, imaging, and quantum technologies.
 
Designing such devices typically involves searching through enormous parameter spaces while repeatedly running computationally intensive electromagnetic simulations.
 
For advanced photonics research, the computational burden can become overwhelming.
 
A single optimization campaign may require thousands of simulations, each demanding significant CPU or GPU resources. As device complexity increases, the associated HPC requirements grow accordingly.
 
The Chalmers approach dramatically reduces that burden.
 
Because the neural network understands the underlying physics, it can accurately predict device performance with far fewer training examples than conventional machine-learning models. This translates directly into lower computational costs and faster development cycles.

The rise of physics-informed AI

The research reflects a broader movement across scientific computing.
 
For years, the dominant trend in AI has been scaling: larger models, larger datasets, and larger computational budgets. Scientific computing is beginning to explore a different path.
 
Instead of relying solely on more data, researchers are increasingly embedding scientific knowledge directly into machine-learning systems.
 
The advantages are substantial:
  • Improved accuracy
  • Better interpretability
  • Reduced training requirements
  • Stronger adherence to physical laws
  • Lower computational costs
For HPC centers facing ever-growing demand for simulation resources, these efficiencies could become increasingly valuable.
 
Rather than replacing supercomputers, physics-informed AI extends its reach by transforming expensive simulation results into reusable computational knowledge.

A new role for supercomputing

The implications extend well beyond photonics.
 
Many of the grand challenges tackled by modern supercomputers involve simulations that are both computationally expensive and physics-rich:
  • Materials discovery
  • Semiconductor design
  • Aerospace engineering
  • Energy systems
  • Climate science
  • Quantum device development
  • Advanced manufacturing
Each field generates vast quantities of simulation data that could potentially be transformed into intelligent surrogate models.
 
In this vision, supercomputers evolve from engines of calculation into engines of knowledge generation.
 
Their role shifts from repeatedly solving the same equations toward training AI systems capable of applying that knowledge across millions of new scenarios.

The curious future of scientific discovery

What makes the Chalmers work particularly fascinating is that it offers a glimpse of a future where AI and supercomputing become inseparable partners.
 
The next scientific breakthrough may not come from a larger neural network alone, nor from a faster supercomputer operating in isolation.
 
Instead, it may emerge from systems in which supercomputers teach AI the fundamental laws governing the physical world, and AI returns the favor by making that knowledge instantly accessible to researchers.
 
The Chalmers “digital super brain” represents an early example of this emerging paradigm, a future where computational science is accelerated not only by more processing power, but by machines that learn directly from physics itself.
 
For the HPC community, that may be the most significant discovery of all.
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From DNA to digital twins: New MDNA framework brings AI supercomputing closer to whole-cell simulation

Tyler O'Neal, Staff Editor LATEST
The next major breakthrough in computational biology may not come from a new supercomputer, but from the software that allows scientists to harness one.
 
Researchers have introduced MDNA, a new open-source molecular modeling framework designed to generate, manipulate, and analyze complex DNA structures with unprecedented flexibility. While the software itself is a biological modeling tool, its broader significance lies in how it could accelerate large-scale molecular simulations, AI-driven biological discovery, and ultimately the long-standing ambition of constructing digital twins of living systems.
 
At a time when exaflops computing is transforming fields ranging from climate science to astrophysics, biology is increasingly becoming one of the most computationally demanding scientific disciplines. DNA is no longer viewed simply as a sequence of genetic letters. It is a dynamic three-dimensional structure whose geometry, interactions, and modifications influence everything from gene expression to disease progression.
 
Understanding those behaviors requires simulation at an extraordinary scale.

Building better starting points for supercomputing

Modern molecular dynamics simulations can model systems containing millions, or even billions, of atoms. Researchers have already demonstrated billion-atom DNA simulations on leadership-class supercomputers, revealing how genes fold, interact, and regulate biological activity.
 
However, one persistent challenge has been constructing biologically realistic DNA configurations suitable for large-scale simulation.
 
MDNA addresses that bottleneck.
 
The framework enables researchers to generate DNA structures with arbitrary shapes using spline-based modeling techniques, while also supporting biologically important modifications such as DNA methylation, Hoogsteen base-pair transitions, and non-canonical nucleotide configurations. By integrating structure generation and structural analysis within a single Python-based workflow, the software streamlines the creation of simulation-ready DNA systems.
 
The result is a platform that reduces the time required to translate a biological hypothesis into a computational experiment.

Bridging AI and molecular simulation

One of the most compelling aspects of MDNA is its compatibility with many computational tools already used across the molecular simulation community.
 
The software integrates with established platforms such as OpenMM, MDAnalysis, MDTraj, oxDNA, Bio3D, HTMD, and PLUMED, making it easier to connect AI-generated molecular designs with high-performance simulation workflows. According to the authors, the goal is not merely to construct DNA structures, but to enable a complete computational ecosystem for studying DNA-protein interactions and molecular dynamics.
 
This arrives at a pivotal moment for computational biology.
 
Artificial intelligence is increasingly being used to design biological molecules, predict molecular structures, and explore vast biochemical design spaces. Recent advances have demonstrated AI-driven approaches to genetic circuit design and biomolecular engineering, generating datasets and candidate structures at scales impossible for human researchers alone.
 
Yet AI predictions are only the beginning.
 
Before a new biological design can be trusted, it often must be validated through detailed molecular simulations that capture physical behavior at atomic resolution. These simulations frequently require the resources of modern supercomputing facilities.
 
MDNA provides a bridge between those two worlds.

Toward digital twins of biology

The implications extend well beyond DNA modeling.
 
Scientists increasingly envision a future in which entire biological systems can be represented as computational “digital twins,” virtual counterparts capable of predicting molecular behavior, disease progression, or therapeutic outcomes before laboratory experiments are performed.
 
Recent projects have mapped the four-dimensional organization of the human genome with unprecedented detail, identifying hundreds of thousands of genomic interactions across time and space.
 
At the same time, researchers are developing computational frameworks capable of simulating cellular processes, cancer evolution, and molecular communication networks.
 
Such ambitions depend on accurate structural models as foundational inputs.
 
MDNA represents one piece of that larger puzzle: a software layer capable of generating realistic DNA architectures that can be incorporated into increasingly sophisticated simulations.

The road to whole-cell simulation

Perhaps the most inspiring aspect of the work is what it suggests about the future.
 
For decades, biologists have dreamed of creating computational models capable of simulating entire living cells. Achieving that goal requires integrating DNA, proteins, RNA, membranes, molecular machinery, and environmental interactions into unified computational frameworks.
 
Exaflops supercomputers are beginning to provide the raw computational horsepower needed for such efforts. Yet hardware alone is insufficient. Researchers also require software capable of building, organizing, and analyzing the immense biological structures that those machines will simulate.
 
MDNA helps fill that gap.
 
By simplifying the construction of highly detailed DNA systems and integrating them with modern simulation ecosystems, the framework contributes to the growing software infrastructure underpinning next-generation computational biology.

A new era for computational life sciences

While the history of supercomputing is often defined by raw hardware power, scientific progress increasingly relies on the sophisticated software frameworks that translate that capacity into actionable insight.
 
MDNA exemplifies this shift: although it may not be the largest or most intensive platform, its value lies in its ability to bridge the gap between AI-driven discovery and large-scale molecular simulation.
 
By simplifying the complexity of DNA modeling, MDNA provides a vital tool for the long-term goal of building biological digital twins.
 
As we enter the exaflops era, such software will be indispensable, proving that while the future of life sciences is written in DNA, it will be mapped through the power of advanced computational modeling.