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[1]
New AI cracks complex engineering problems faster than supercomputers
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. Details about the research appear in Nature Computational Science. Called DIMON (Diffeomorphic Mapping Operator Learning), the framework solves ubiquitous math problems known as partial differential equations that are present in nearly all scientific and engineering research. Using these equations, researchers can translate real-world systems or processes into mathematical representations of how objects or environments will change over time and space. "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," said Natalia Trayanova, a Johns Hopkins University biomedical engineering and medicine professor who co-led the research. "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." In addition to demonstrating the applicability of DIMON in solving other engineering problems, Trayanova's team tested the new AI on over 1,000 heart "digital twins," highly detailed computer models of real patients' hearts. The platform was able to predict how electrical signals propagated through each unique heart shape, achieving high prognostic accuracy. 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. "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," said 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." Partial differential equations are generally solved by breaking complex shapes like airplane wings or body organs into grids or meshes made of small elements. The problem is then solved on each simple piece and recombined. But if these shapes change -- like in crashes or deformations -- the grids must be updated and the solutions recalculated, which can be computationally slow and expensive. 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. The team is incorporating into the DIMON framework cardiac pathology that leads to arrhythmia. Because of its versatility, the technology can be applied to shape optimization and many other engineering tasks where solving partial differential equations on new shapes is repeatedly needed, said Minglang Yin, a Johns Hopkins Biomedical Engineering Postdoctoral Fellow who developed the platform. "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," Yin said. "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." Other authors are Nicolas Charon of University of Houston, Ryan Brody and Mauro Maggioni (co-lead) of Johns Hopkins, and Lu Lu of Yale University. This work is supported by NIH grants R01HL166759 and R01HL174440; a grant from the Leducq Foundations; the Heart Rhythm Society Fellowship; U.S. Department of Energy grants DE-SC0025592 and DE-SC0025593; NSF grants DMS-2347833, DMS-1945224, and DMS-2436738; and Air Force Research Laboratory awards FA9550-20-1-0288, FA9550-21-1-0317, and FA9550-23-1-0445.
[2]
New AI cracks complex engineering problems faster than supercomputers
Modeling how cars deform in a crash, how spacecraft respond 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. Details about the research appear in Nature Computational Science. Called DIMON (Diffeomorphic Mapping Operator Learning), the framework solves ubiquitous math problems known as partial differential equations that are present in nearly all scientific and engineering research. Using these equations, researchers can translate real-world systems or processes into mathematical representations of how objects or environments will change over time and space. "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," said Natalia Trayanova, a Johns Hopkins University biomedical engineering and medicine professor who co-led the research. "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." In addition to demonstrating the applicability of DIMON in solving other engineering problems, Trayanova's team tested the new AI on over 1,000 heart "digital twins," highly detailed computer models of real patients' hearts. The platform was able to predict how electrical signals propagated through each unique heart shape, achieving high prognostic accuracy. 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. "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," said 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." Partial differential equations are generally solved by breaking complex shapes like airplane wings or body organs into grids or meshes made of small elements. The problem is then solved on each simple piece and recombined. But if these shapes change -- like in crashes or deformations -- the grids must be updated and the solutions recalculated, which can be computationally slow and expensive. 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. The team is incorporating into the DIMON framework cardiac pathology that leads to arrhythmia. Because of its versatility, the technology can be applied to shape optimization and many other engineering tasks where solving partial differential equations on new shapes is repeatedly needed, said Minglang Yin, a Johns Hopkins Biomedical Engineering Postdoctoral Fellow who developed the platform. "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," Yin said. "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." Other authors are Nicolas Charon of University of Houston, Ryan Brody and Mauro Maggioni (co-lead) of Johns Hopkins, and Lu Lu of Yale University.
[3]
New AI solves math and science problems faster than supercomputers
These problems, called partial differential equations, are the backbone of engineering and science. But solving them can take days, even weeks, especially for complex shapes. Now, Johns Hopkins University researchers have created a new AI model called DIMON. It can solve these complex equations thousands of times faster, right on your personal computer. "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," said Natalia Trayanova, biomedical engineering and medicine professor from the Johns Hopkins University. Partial differential equations are common mathematical problems. These equations help convert real-world scenarios into mathematical models to predict future changes in objects or environments. However, solving these big math problems is typically a job for supercomputers. Things are becoming easy with the arrival of artificial intelligence. This new AI framework DIMON stands for Diffeomorphic Mapping Operator Learning.
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Johns Hopkins researchers develop DIMON, an AI framework that solves complex partial differential equations thousands of times faster than supercomputers, potentially transforming various fields of engineering and medical diagnostics.
Researchers at Johns Hopkins University have developed a groundbreaking AI framework called DIMON (Diffeomorphic Mapping Operator Learning) that promises to revolutionize the way complex engineering problems are solved. This innovative technology can tackle massive mathematical challenges on personal computers, outperforming traditional supercomputers in both speed and efficiency 1.
DIMON specializes in solving partial differential equations, which are fundamental to nearly all scientific and engineering research. These equations are used to create mathematical models of real-world systems, predicting how objects or environments change over time and space 2.
The framework's versatility allows it to be applied across various fields, including:
One of the most promising applications of DIMON is in the field of medical diagnostics, particularly in cardiology. The research team, led by Professor Natalia Trayanova, tested the AI on over 1,000 heart "digital twins" - detailed computer models of real patients' hearts 1.
The impact of DIMON on computational speed is staggering:
This dramatic reduction in processing time could transform the daily clinical workflow, allowing for rapid diagnosis and treatment planning for conditions like cardiac arrhythmia 3.
Unlike traditional methods that break complex shapes into grids or meshes, DIMON uses AI to understand how physical systems behave across different shapes. This approach eliminates the need for constant recalculation when shapes change, making it significantly faster and more efficient 2.
The versatility of DIMON extends beyond its current applications. Researchers are already incorporating cardiac pathology into the framework to study arrhythmia. Its potential uses include:
This groundbreaking research is the result of collaboration between experts from Johns Hopkins University, the University of Houston, and Yale University. The project has received support from various organizations, including the NIH, the Leducq Foundation, and the U.S. Department of Energy 1.
As DIMON continues to evolve, its impact on scientific research, engineering, and medical diagnostics is expected to be transformative, potentially ushering in a new era of computational problem-solving across multiple disciplines.
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