A new artificial intelligence (AI) system could make solving complex engineering problems thousands of times faster, even on ordinary computers.
From understanding how cars crumple in a crash to predicting how spacecraft handle extreme environments, this AI breakthrough offers faster, cheaper, and more accessible solutions than supercomputers.
The AI framework, called DIMON (Diffeomorphic Mapping Operator Learning), can quickly solve difficult mathematical problems known as partial differential equations.
These equations are used in science and engineering to model real-world processes, such as how fluids flow, heat spreads, or materials deform over time.
The research was published in Nature Computational Science and led by Natalia Trayanova, a biomedical engineering professor at Johns Hopkins University.
She explained that DIMON is a versatile tool that can be applied to countless fields.
“This framework can tackle problems in any area of science or engineering where these equations need solving—like crash testing, designing medical implants, or understanding how forces interact with complex shapes,” Trayanova said.
One of DIMON’s most exciting applications is in heart health.
Researchers tested it on “digital twins” of over 1,000 patients’ hearts—detailed computer models that simulate how electrical signals move through the heart.
DIMON predicted these signals with impressive accuracy, helping doctors identify patients at risk of deadly heart arrhythmias in just 30 seconds. Previously, such calculations could take days and required supercomputers.
Now, it can be done on a standard desktop, bringing advanced medical technology closer to everyday use.
The traditional method for solving partial differential equations involves breaking objects—like an airplane wing or a human heart—into small grids.
The problem is solved piece by piece and reassembled. However, if the object changes shape, the calculations must start over, which is time-consuming and expensive.
DIMON bypasses this by using AI to “learn” how different shapes behave. Instead of recalculating everything, it predicts the results based on patterns it has already learned.
Beyond medicine, DIMON could improve the design of cars, bridges, and even aircraft by quickly optimizing shapes and materials.
“DIMON can solve problems for one shape and adapt its solution to many others, making it incredibly versatile,” said Minglang Yin, a Johns Hopkins postdoctoral researcher who developed the platform.
This powerful AI tool may soon help scientists and engineers across industries solve problems faster, cheaper, and more efficiently than ever before.
Source: Johns Hopkins University.
