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

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 ...

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

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 ...

Researchers at IISc and University College London have developed a machine learning method to predict material properties using limited data. Their transfer learning-based model effectively ...

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.