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

Washington University in St. Louis. "Machine learning generates 3D model from 2D pictures." ScienceDaily. ScienceDaily, 19 September 2022. <www.sciencedaily.com / releases / 2022 / 09 ...

Researchers at NVIDIA have come up with a clever machine learning technique for taking 2D images and fleshing them out into 3D models. Normally this happens in reverse—these days, it's not all ...

Researchers developed a two-stage ML model to predict coating degradation by linking environmental factors to physical ...

An article recently published in the journal Advanced Theory and Simulations discussed a rough identification method to determine the thickness of 2D materials using new deep learning approaches. The ...

Fabs and mask shops also use classical machine learning in the ‘big data’ analysis of all the operation data available to look for ways to improve yield and prevent downtime.” Now, some are exploring ...

This is explained by the importance of these newly synthesized materials, with applications ranging from energy to optics, electronics, catalysis, bio-detection, and structural materials. Advancing ...

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.