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Found 12 papers, 1 papers with code
Collaborative Pareto Set Learning in Multiple Multi-Objective Optimization Problems
This collaborative approach enables CoPSL to efficiently learn the Pareto sets of multiple MOPs in a single execution while leveraging the potential relationships among various MOPs.
Road Surface Defect Detection -- From Image-based to Non-image-based: A Survey
As a result, there has been a growing interest in the literature on the subject, leading to the development of various road surface defect detection methods.
Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Models
Sixty proposed various strategies for mitigating biases, especially targeting implicit and selection biases.
Large-scale global optimization of ultra-high dimensional non-convex landscapes based on generative neural networks
We present a non-convex optimization algorithm metaheuristic, based on the training of a deep generative network, which enables effective searching within continuous, ultra-high dimensional landscapes.
WaveY-Net: Physics-augmented deep learning for high-speed electromagnetic simulation and optimization
The calculation of electromagnetic field distributions within structured media is central to the optimization and validation of photonic devices.
Vision-Guided Active Tactile Perception for Crack Detection and Reconstruction
In recent years, computer vision techniques have been applied in detecting cracks in concrete structures.
Multi-objective and categorical global optimization of photonic structures based on ResNet generative neural networks
We show that deep generative neural networks, based on global topology optimization networks (GLOnets), can be configured to perform the multi-objective and categorical global optimization of photonic devices.
Deep neural networks for the evaluation and design of photonic devices
The data sciences revolution is poised to transform the way photonic systems are simulated and designed.
Progressive-Growing of Generative Adversarial Networks for Metasurface Optimization
Generative adversarial networks, which can generate metasurfaces based on a training set of high performance device layouts, have the potential to significantly reduce the computational cost of the metasurface design process.
Simulator-based training of generative models for the inverse design of metasurfaces
Metasurfaces are subwavelength-structured artificial media that can shape and localize electromagnetic waves in unique ways.
Global optimization of dielectric metasurfaces using a physics-driven neural network
We present a global optimizer, based on a conditional generative neural network, which can output ensembles of highly efficient topology-optimized metasurfaces operating across a range of parameters.
Freeform Diffractive Metagrating Design Based on Generative Adversarial Networks
A key challenge in metasurface design is the development of algorithms that can effectively and efficiently produce high performance devices.
