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Found 7 papers, 4 papers with code
Materials science in the era of large language models: a perspective
Large Language Models (LLMs) have garnered considerable interest due to their impressive natural language capabilities, which in conjunction with various emergent properties make them versatile tools in workflows ranging from complex code generation to heuristic finding for combinatorial problems.
SAMBA: A Trainable Segmentation Web-App with Smart Labelling
Segmentation is the assigning of a semantic class to every pixel in an image and is a prerequisite for various statistical analysis tasks in materials science, like phase quantification, physics simulations or morphological characterization.
HRTF upsampling with a generative adversarial network using a gnomonic equiangular projection
An individualised head-related transfer function (HRTF) is very important for creating realistic virtual reality (VR) and augmented reality (AR) environments.
Two approaches to inpainting microstructure with deep convolutional generative adversarial networks
Imaging is critical to the characterisation of materials.
Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks
In this paper, we present a method for combining information from pairs of distinct but complementary imaging techniques in order to accurately reconstruct the desired multi-phase, high resolution, representative, 3D images.
Generating 3D structures from a 2D slice with GAN-based dimensionality expansion
Generative adversarial networks (GANs) can be trained to generate 3D image data, which is useful for design optimisation.
Pores for thought: The use of generative adversarial networks for the stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries
The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices.
