Pages that link to "Item:Q2160385"

The following pages link to Machine learning for topology optimization: physics-based learning through an independent training strategy (Q2160385):

Displaying 15 items.

• Universal machine learning for topology optimization (Q2022034) (← links)
• Data-driven multifidelity topology design using a deep generative model: application to forced convection heat transfer problems (Q2060177) (← links)
• Compliance minimisation of smoothly varying multiscale structures using asymptotic analysis and machine learning (Q2142133) (← links)
• Obey validity limits of data-driven models through topological data analysis and one-class classification (Q2147924) (← links)
• A machine learning framework for accelerating the design process using CAE simulations: an application to finite element analysis in structural crashworthiness (Q2237726) (← links)
• Machine learning-combined topology optimization for functionary graded composite structure design (Q2246382) (← 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)
• Robust topology optimization with low rank approximation using artificial neural networks (Q2667321) (← links)
• Deep energy method in topology optimization applications (Q2694685) (← links)
• Current and future trends of artificial intelligence in the field of structural topology optimization (Q3385174) (← links)
• A deep convolutional neural network for topology optimization with perceptible generalization ability (Q6048164) (← links)
• Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties (Q6099231) (← links)
• Real-time topology optimization via learnable mappings (Q6589328) (← links)
• Deep learning in computational mechanics: a review (Q6604128) (← links)