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Category:All Headlines World & Geopolitics Markets & Economy Tech & AI PoliticsSearch results for: "Deep material network via" (30 stories)

Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy

Lead StoryNaturetech

Jul 25, 2023

Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy

<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE5zVDVhczgxMlYxclV5S1E1TWFraTNoZjdEbFFpbmJuOUVHRmlGcnRaaWxuRjJlUGdmNW1UUzFpSGFXT2h0ZjJNVnhwUnE0LTlZaXVHbk1OM0lLaVJHTGhZ?oc=5" target="_blank">Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy</a>&nbsp;&nbsp;Nature

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Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics

Frontiersgeneral

Jun 25, 2024

Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics

<a href="https://news.google.com/rss/articles/CBMikAFBVV95cUxOQ1M2T1BrNXNtSFZmUUpjb0M2SEJaRlZ0YlAxcG5uS1YxbFl0OHQzYl9RN3RGcUp3cTNpcHVfLUJoODY2OHprb20wcUg5dEgwcmNzSXJhekFwUnhMNHdEWE1heVBwbER6YzZ2RElSYkpRUkxOck9qeWVISjNQSmNEdFZfS1R6aE9mLXdsOVhXX1U?oc=5" target="_blank">Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics</a>&nbsp;&nbsp;Frontiers

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Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy

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Search Results for "Deep material network via"

Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics

A deep neural network approach for predicting the melting point of low-molecular phase change materials

Rethinking materials simulations: Blending direct numerical simulations with neural operators

Networking autonomous material exploration systems through transfer learning | npj Computational Materials

Predicting stress, strain and deformation fields in materials and structures with graph neural networks

Deep neural network for phonon-assisted optical spectra of semiconductors at finite temperatures

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Deep learning-based inverse design of microstructured materials for optical optimization and thermal radiation control

XElemNet: towards explainable AI for deep neural networks in materials science

Fatigue life prediction of composite materials using strain distribution images and a deep convolution neural network

Scaling deep learning for materials discovery

Recent advances and applications of deep learning methods in materials science | npj Computational Materials

Bridging deep learning force fields and electronic structures with a physics-informed approach

Neural networks determination of material elastic constants and structures in nematic complex fluids

Training all-mechanical neural networks for task learning through in situ backpropagation

Deep learning based analysis of microstructured materials for thermal radiation control

A generalised deep learning-based surrogate model for homogenisation utilising material property encoding and physics-based bounds

Ultrafast laser ablation simulator using deep neural networks

Improving deep learning model performance under parametric constraints for materials informatics applications

DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules

Transfer learning of recurrent neural network‐based plasticity models

All-optical machine learning using diffractive deep neural networks

Deep learning for property prediction of natural fiber polymer composites

Analyses of internal structures and defects in materials using physics-informed neural networks

Viscosity in water from first-principles and deep-neural-network simulations

Deep learning artificial neural network framework to optimize the adsorption capacity of 3-nitrophenol using carbonaceous material obtained from biomass waste

Graph neural networks for materials science and chemistry - Communications Materials

Inverse design of glass structure with deep graph neural networks

Enhanced detection of threat materials by dark-field x-ray imaging combined with deep neural networks