Modeling how cars deform in a crash, how spacecraft responds to extreme environments, or how bridges resist stress could be made thousands of times faster thanks to new artificial intelligence that enables personal computers to solve massive math problems that generally require supercomputers.
The new AI framework is a generic approach that can quickly predict solutions to pervasive and time-consuming math equations needed to create models of how fluids or electrical currents propagate through different geometries, like those involved in standard engineering testing.Called DIMON , the framework solves ubiquitous math problems known as partial differential equations that are present in nearly all scientific and engineering research.
Trayanova's team relies on solving partial differential equations to study cardiac arrhythmia, which is an electrical impulse misbehavior in the heart that causes irregular beating. With their heart digital twins, researchers can diagnose whether patients might develop the often-fatal condition and recommend ways to treat it.
DIMON solves that problem by using AI to understand how physical systems behave across different shapes, without needing to recalculate everything from scratch for each new shape. Instead of dividing shapes into grids and solving equations over and over, the AI predicts how factors such as heat, stress, or motion will behave based on patterns it has learned, making it much faster and more efficient in tasks like optimizing designs or modeling shape-specific scenarios.
Other authors are Nicolas Charon of University of Houston, Ryan Brody and Mauro Maggioni of Johns Hopkins, and Lu Lu of Yale University.
Energy Technology Civil Engineering Engineering And Construction Computer Modeling Mathematics Educational Technology Mathematical Modeling
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