Machine learning for morphable materials New platform can program the transformation of 2D stretchable surfaces into specific 3D shapes Date: January 12, 2022 Source: Harvard John A. Paulson ...

A neural field network can create a continuous 3D model from a limited number of 2D images, and it does it without being trained on other samples.

Researchers have uncovered how primate brains transform flat, 2D visual inputs into rich, 3D mental representations of ...

Microscopic images of 2D materials were processed via three deep-learning architectures for classification, segmentation, and detection.

The global market for 2D materials — already estimated at several billion dollars annually — is growing at a 4 percent rate. This is explained by the importance of these newly synthesized materials, ...

Magnetic materials are in high demand. They're essential to the energy storage innovations on which electrification depends ...

Conventional handheld photoacoustic and ultrasound Imaging (PAUS), while offering flexibility, offers only a narrow view of the target region, providing limited information on its structure.

Researchers integrate a detector system with a 2D polaritonic platform, enabling the detection of 2D polaritonic nanoresonators with spectral resolution, high-quality factors, and miniaturization.

According to new research from Drexel University, current methods for detecting manipulated digital media will not be effective against AI-generated video; but a machine-learning approach could be the ...

A neural field network developed at Washington University in St. Louis, can create a continuous 3D model from a limited number of 2D images, and it does it without being trained on other samples.