Pages that link to "Item:Q2022034"

The following pages link to Universal machine learning for topology optimization (Q2022034):

Displaying 20 items.

• De-homogenization using convolutional neural networks (Q2060089) (← links)
• Data-driven multifidelity topology design using a deep generative model: application to forced convection heat transfer problems (Q2060177) (← links)
• A phase field method based on multi-level correction for eigenvalue topology optimization (Q2096872) (← links)
• Level set-based topology optimization for thermal-fluid system based on the radial basis functions (Q2110835) (← links)
• Compliance minimisation of smoothly varying multiscale structures using asymptotic analysis and machine learning (Q2142133) (← links)
• Learning finite element convergence with the multi-fidelity graph neural network (Q2145122) (← links)
• Obey validity limits of data-driven models through topological data analysis and one-class classification (Q2147924) (← links)
• A phase field-based systematic multiscale topology optimization method for porous structures design (Q2157106) (← links)
• Machine learning for topology optimization: physics-based learning through an independent training strategy (Q2160385) (← links)
• A machine learning framework for accelerating the design process using CAE simulations: an application to finite element analysis in structural crashworthiness (Q2237726) (← links)
• TONR: an exploration for a novel way combining neural network with topology optimization (Q2246269) (← links)
• Accurate and real-time structural topology prediction driven by deep learning under moving morphable component-based framework (Q2247294) (← links)
• Clustering discretization methods for generation of material performance databases in machine learning and design optimization (Q2319387) (← links)
• Cross-resolution acceleration design for structural topology optimization based on deep learning (Q3385176) (← links)
• Robust, strong form mechanics on an adaptive structured grid: efficiently solving variable-geometry near-singular problems with diffuse interfaces (Q6084789) (← links)
• Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties (Q6099231) (← links)
• Material Design with Topology Optimization Based on the Neural Network (Q6173001) (← links)
• Dynamically configured physics-informed neural network in topology optimization applications (Q6550166) (← links)
• Machine learning in solid mechanics: application to acoustic metamaterial design (Q6592358) (← links)
• Deep learning in computational mechanics: a review (Q6604128) (← links)