Brain-Inspired Computing Solves Complex Equations for Faster, Efficient Supercomputers

Neuromorphic computers demonstrate ability to efficiently solve partial differential equations, promising energy-efficient computing breakthroughs.

Published on Feb. 16, 2026

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Researchers at Sandia National Laboratories have developed a novel algorithm that allows neuromorphic hardware to efficiently solve complex partial differential equations (PDEs) - the mathematical foundation for modeling phenomena like fluid dynamics and structural mechanics. This breakthrough could pave the way for the first neuromorphic supercomputer, offering significant energy savings compared to traditional supercomputer systems.

Why it matters

The potential for energy savings is a key driver of this research, as the National Nuclear Security Administration relies on energy-intensive supercomputers to simulate complex physics scenarios for maintaining the nation's nuclear deterrent. Neuromorphic computing offers a path to significantly reduce energy consumption while maintaining computational performance.

The details

The algorithm developed by computational neuroscientists Brad Theilman and Brad Aimone at Sandia closely mirrors the structure and behavior of cortical networks, suggesting a fundamental link between brain function and mathematical problem-solving. This connection could have implications for understanding and treating neurological disorders, as the researchers suggest that diseases of the brain might be, at their core, diseases of computation.

  • The research findings were published in Nature Machine Intelligence in February 2026.

The players

Sandia National Laboratories

A U.S. Department of Energy national laboratory focused on research and development in national security, energy, and environmental technologies.

Brad Theilman

A computational neuroscientist at Sandia National Laboratories who co-developed the novel algorithm for neuromorphic computers to solve complex partial differential equations.

Brad Aimone

A computational neuroscientist at Sandia National Laboratories who co-developed the novel algorithm for neuromorphic computers to solve complex partial differential equations.

National Nuclear Security Administration

The agency responsible for maintaining the United States' nuclear deterrent, which relies on energy-intensive supercomputers for complex physics simulations.

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What they’re saying

“You can solve real physics problems with brain-like computation. That's something you wouldn't expect because people's intuition goes the opposite way. And in fact, that intuition is often wrong.”

— Brad Aimone, Computational Neuroscientist, Sandia National Laboratories (Nature Machine Intelligence)

“We based our circuit on a relatively well-known model in the computational neuroscience world. We've shown the model has a natural but non-obvious link to PDEs, and that link hasn't been made until now.”

— Brad Theilman, Computational Neuroscientist, Sandia National Laboratories (Nature Machine Intelligence)

What’s next

Continued refinement of algorithms like the one developed by Theilman and Aimone, as well as advancements in neuromorphic hardware and increased collaboration between mathematicians, neuroscientists, and engineers, will be crucial for expanding the capabilities and adoption of neuromorphic computing.

The takeaway

This research demonstrates the potential for brain-inspired computing to revolutionize scientific and engineering problem-solving, offering significant energy savings and insights into the fundamental nature of brain function and computation.