Human beings can do a lot of things really well, but at the same time, there is nothing we do better than improving on a consistent basis. This tendency to improve, no matter the situation has brought the world some huge milestones, with technology emerging as quite a major member of the group. The reason why we hold technology in such a high regard is, by and large, predicated upon its skill-set, which ushered us towards a reality that nobody could have ever imagined otherwise. Nevertheless, if we look beyond the surface for a second, it will become clear how the whole runner was also very much inspired from the way we applied those skills across a real world environment. The latter component, in fact, did a lot to give the creation a spectrum-wide presence, and as a result, kickstart a full-blown tech revolution. Of course, this revolution then went on to scale up the human experience through some outright unique avenues, but even after achieving a feat so notable, technology will somehow continue to bring forth the right goods. The same has turned more and more evident in recent times, and assuming a new discovery ends up with the desired impact, it will only put that trend on a higher pedestal moving forward.

The researching team at Massachusetts Institute of Technology has successfully developed a system, which can be used to explore the interior of different materials from the outside. According to certain reports, the team used one machine learning iteration known as deep learning to compare large stimulated datasets about materials’ external force fields and the corresponding internal structure. This, in turn, would create a rather informed basis to then let the stated system make accurate predictions of the interior, and like it was promised, it will be able to do so just by gauging the relevant surface data. Interestingly enough, while the researchers did, at length, used uniform materials to train the system; they also roped in stuff that was basically a combination of different materials. Leave that, the team even used, to an extent, those measurements of surface properties, including stresses, and electric and magnetic fields. Hence, owing to its notably versatile origins, the system can deliver value across what is a pretty broad horizon.

“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,” said Markus Buehler, professor of civil and environmental engineering.

To achieve the stated accuracy, though, the researchers first placed the system’s preliminary predictions alongside actual data on the material in question. Once they eliminated the differences between results, the model was again tested against cases where materials are well enough understood, thus making it easier to calculate the true internal properties. Going by the published results, the new method’s predictions managed to mirror the calculated properties at a pretty encouraging rate.

Mind you, many other studies have tried using X-rays and other similar imaging techniques for fulfilling a similar goal, but despite our constant efforts, such techniques remain an expensive and logistically inconvenient alternative.

Talk about the technology’s immediate future, the team is expecting it to be used mostly in laboratory setting, with one possible application centered on testing materials used for soft robotics applications.

Commenting on the plan going forward, Buehler said:

“We can measure things on the surface, but we have no idea what’s going on a lot of times inside the material, because it’s made out of a hydrogel or proteins or biomaterials for actuators, and there’s no theory for that. So, that’s an area where researchers could use our technique to make predictions about what’s going on inside, and perhaps design better grippers or better composites,”