![]() “How can we recover this missing information?” he wondered. Yang says that he initially started thinking about this approach when he was studying data on a material where part of the imagery he was using was blurred, and he wondered how it might be possible to “fill in the blank” of the missing data in the blurred area. It is “very universal, not just for different materials, but also for different disciplines.” “It is not just limited to solid mechanics problems, but it can also be applied to different engineering disciplines, like fluid dynamics and other types.” Buehler adds that it can be applied to determining a variety of properties, not just stress and strain, but fluid fields or magnetic fields, for example the magnetic fields inside a fusion reactor. Yang says that the method they developed is broadly applicable. We don’t have a theory for it, but if we have enough data collected, we can train the model.” ![]() “With complex biological tissue, we don’t understand exactly how it behaves, but we can measure the behavior. The technique works even for materials whose complexity is not fully understood, he says. “And of course, in biology as well, any kind of biological material will be made out of multiple components and they have very different properties, like in bone, where you have very soft protein, and then you have very rigid mineral substances.” “Some new airplanes are made out of composites, so they have deliberate designs of having different phases,” Buehler says. This included not only uniform materials but also ones with different materials in combination. ![]() The technique they developed involved training an AI model using vast amounts of data about surface measurements and the interior properties associated with them. To deal with that ambiguity, “we have created methods that can give us all the possibilities, all the options, basically, that might result in this particular scenario.” For example, many different internal configurations might exhibit the same surface properties. “Is there disease in there, or some kind of growth or changes in the tissue?” The aim was to develop a system that could answer these kinds of questions in a completely noninvasive way.Īchieving that goal involved addressing complexities including the fact that “many such problems have multiple solutions,” Buehler says. The same kind of questions can apply to biological tissues as well, he adds. ![]() “So, what we have done is basically ask the question: Can we develop an AI algorithm that could look at what’s going on at the surface, which we can easily see either using a microscope or taking a photo, or maybe just measuring things on the surface of the material, and then trying to figure out what’s actually going on inside?” That inside information might include any damages, cracks, or stresses in the material, or details of its internal microstructure. It's also possible to use X-rays and other techniques, but these tend to be expensive and require bulky equipment, he says. The only way you can do that is by cutting it and then looking inside and seeing if there’s any kind of damage in there.” But you can’t really look inside the material. “If you have a piece of material - maybe it’s a door on a car or a piece of an airplane - and you want to know what’s inside that material, you might measure the strains on the surface by taking images and computing how much deformation you have. “It’s a very common problem in engineering,” Buehler explains. The results are being published in the journal Advanced Materials, in a paper by doctoral student Zhenze Yang and professor of civil and environmental engineering Markus Buehler. The team used a type of machine learning known as deep learning to compare a large set of simulated data about materials’ external force fields and the corresponding internal structure, and used that to generate a system that could make reliable predictions of the interior from the surface data. Their new approach allows engineers to figure out what’s going on inside simply by observing properties of the material’s surface. Maybe you can’t tell a book from its cover, but according to researchers at MIT you may now be able to do the equivalent for materials of all sorts, from an airplane part to a medical implant.
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