Researchers from Johns Hopkins University have developed DIMON — a new AI that solves complex partial differential equations at unprecedented speeds, promising transformative impacts on engineering and medical fields.
Engineering and medical research are on the brink of a revolution thanks to a novel AI framework called DIMON (Diffeomorphic Mapping Operator Learning). Developed by a team at Johns Hopkins University, this groundbreaking technology enables personal computers to swiftly solve complex mathematical problems, a task traditionally reserved for supercomputers.
The innovation, detailed in an article published in the journal Nature Computational Science, has wide-ranging implications for fields requiring modeling of dynamic geographical and structural stress responses — from crash testing and spacecraft behavior to cardiac health diagnostics and treatment.
“While the motivation to develop it came from our own work, this is a solution that we think will have generally a massive impact on various fields of engineering because it’s very generic and scalable,” co-lead author Natalia Trayanova, a biomedical engineering and medicine professor at Johns Hopkins University, said in a news release. “It can work basically on any problem, in any domain of science or engineering, to solve partial differential equations on multiple geometries, like in crash testing, orthopedics research or other complex problems where shapes, forces and materials change.”
DIMON addresses a pervasive issue in computational modeling: the need for repeated recalculations when shapes or conditions change. Traditional methods involve breaking down complex shapes into grids or meshes and solving the equations piecemeal — a laborious process that demands significant computational power and time.
The new AI framework, however, learns from patterns in physical systems, enabling it to predict behaviors such as heat, stress and motion without recalculating from scratch. This capability not only speeds up the problem-solving process but also makes it vastly more efficient.
Trayanova’s team has already tested DIMON on over 1,000 heart “digital twins” — detailed computer models of patients’ hearts. The AI demonstrated high prognostic accuracy in predicting how electrical signals propagate through each unique heart shape, which is crucial for diagnosing and treating cardiac arrhythmia.
“We’re bringing novel technology into the clinic, but a lot of our solutions are so slow it takes us about a week from when we scan a patient’s heart and solve the partial differential equations to predict if the patient is at high risk for sudden cardiac death and what is the best treatment plan,” added Trayanova, who directs the Johns Hopkins Alliance for Cardiovascular Diagnostic and Treatment Innovation. “With this new AI approach, the speed at which we can have a solution is unbelievable. The time to calculate the prediction of a heart digital twin is going to decrease from many hours to 30 seconds, and it will be done on a desktop computer rather than on a supercomputer, allowing us to make it part of the daily clinical workflow.”
This breakthrough could revolutionize medical diagnostics by reducing the time needed to predict patient outcomes and tailor treatment plans. Beyond health care, DIMON’s versatility makes it a game-changer for any field involving complex geometries and dynamic conditions, such as engineering design and optimization.
“For each problem, DIMON first solves the partial differential equations on a single shape and then maps the solution to multiple new shapes. This shape-shifting ability highlights its tremendous versatility,” Minglang Yin, a postdoctoral fellow in biomedical engineering at Johns Hopkins who developed the platform, said in the news release. “We are very excited to put it to work on many problems as well as to provide it to the broader community to accelerate their engineering design solutions.”
This breakthrough in AI technology holds promise to fundamentally alter the landscape of engineering and medical research by vastly accelerating the problem-solving process and reducing reliance on expensive computational resources. DIMON is poised to unlock new possibilities, offering unprecedented speed and efficiency in modeling and predicting real-world behaviors across various scientific domains.