Researchers at Sandia National Laboratories have demonstrated that neuromorphic computers—circuitry designed to mimic the human brain—can efficiently solve complex partial differential equations (PDEs), the foundation for modeling physics from aerodynamics to weapons systems. The breakthrough, published in Nature Machine Intelligence, reveals these energy-efficient systems could pave the way for the first neuromorphic supercomputer, dramatically cutting power for national security simulations.
We build computers to think like us, but what if we built them to work like us? Scientists at Sandia National Laboratories have done just that, proving that brain-inspired hardware is shockingly capable at heavy-duty mathematics. In a new study, computational neuroscientists Brad Theilman and Brad Aimone describe a novel algorithm that allows neuromorphic systems to tackle partial differential equations (PDEs). These equations are the bedrock of scientific computing, used to simulate everything from airflow over a wing to the behavior of nuclear materials. “You can solve real physics problems with brain-like computation,” said Brad Aimone, a researcher at Sandia. “That’s something you wouldn’t expect.”
For decades, the assumption was that neuromorphic computers were best for sensory tasks like image recognition, not rigorous numerical analysis. This research, supported by the Department of Energy and the National Nuclear Security Administration, flips that script. The team developed an algorithm based on a well-known model of cortical networks, creating a “non-obvious link” between neural circuitry and applied mathematics. “We’ve shown the model has a natural but non-obvious link to PDEs, and that link hasn’t been made until now — 12 years after the model was introduced,” Brad Theilman explained, according to the lab’s announcement.
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The implications are profound for energy consumption. Traditional supercomputers used for National Nuclear Security Administration missions, like certifying the nuclear stockpile, are power-hungry beasts. Neuromorphic systems, by contrast, operate on a fraction of the energy by processing information in a massively parallel, event-driven manner—much like our own brains. “The amount of resources that [traditional systems] require is ridiculous, frankly,” Theilman stated. By solving PDEs with brain-like efficiency, this technology offers a path to maintain immense computational power while slashing electricity use.
Beyond efficiency, the research opens a two-way street between neuroscience and computing. The algorithm retains strong similarities to actual brain dynamics, suggesting that studying these systems could also shed light on how our own minds work. “Diseases of the brain could be diseases of computation,” Aimone mused, indicating that neuromorphic research might offer clues to understanding conditions like Alzheimer’s and Parkinson’s.
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The Sandia team now envisions a future where neuromorphic supercomputers are central to national security and scientific discovery. They are actively exploring how to map even more advanced mathematical techniques onto brain-inspired hardware. As Theilman put it, they have “a foot in the door for understanding the scientific questions, but also we have something that solves a real problem.” This breakthrough proves that sometimes, to build a better computer, you just need to look inward.
