AI + Desktop = Supercomputer
DIMON, an AI framework, solves complex engineering and scientific problems in seconds on a desktop that might take hours on a supercomputer.
The latest and greatest advancements in artificial intelligence (AI) may be very impressive, yet building large language models and text-to-image generators has never been the goal. The long-standing dream of researchers in the field has always been to develop an artificial general intelligence or superintelligence that can find the solutions to problems that have eluded humans for centuries. Despite the overblown claims frequently thrown about by publicity seekers, nothing remotely resembling superintelligence has been created to date. There is no particularly compelling reason to believe such a technology is on the way in the near future, either.
Approximately correct is absolutely good enough
An innovation just announced by Johns Hopkins University may not be anything like a true superintelligence, but it appears to be one more step in that direction. The research team has developed a new AI framework that can solve very complex mathematical equations relevant to engineering and scientific research. Using traditional methods, these equations may take exceedingly long periods of time to solve, even with the help of supercomputers. Some problems are so computationally-intensive that they are not practical to solve in any case. But when using the new framework, the equations can be solved on a desktop computer in a matter of seconds.
The framework is named DIMON, which stands for Diffeomorphic Mapping Operator Learning, and it was designed to solve partial differential equations (PDEs) with unprecedented speed and efficiency. These equations are foundational in modeling real-world systems across science and engineering, from predicting how cars deform in crashes to analyzing how electrical signals move through the human heart.
Typically, solving PDEs involves breaking down complex geometries into grids or meshes and recalculating solutions for each new shape — a very time-intensive process requiring supercomputers. DIMON greatly simplifies this by learning patterns of behavior in physical systems and predicting solutions directly, eliminating the need for repetitive recalculations. It operates quickly, even on standard desktop computers, making it highly scalable and accessible for applications like crash testing, shape optimization, and biomedical engineering.
The future of AI is on the edge
To evaluate the system, DIMON was used to analyze over 1,000 digital twin models of patients' hearts to predict how electrical signals traveled through them. It was found to be capable of drastically reducing computation times from several hours on a supercomputer to just 30 seconds on a desktop. This speed enables faster and more practical clinical workflows, such as diagnosing and treating cardiac arrhythmias.
Looking ahead, DIMON’s versatility opens the door to numerous future applications. It could enable the design of safer vehicles through faster crash simulations, enhance the development of more resilient infrastructure like bridges and buildings, and optimize aerospace designs for efficiency and durability. Furthermore, DIMON’s ability to rapidly solve complex equations may accelerate advancements in energy systems, environmental modeling, and materials science, making it a valuable tool across countless scientific and engineering disciplines.