RIGID uses a random forest forward model and MCMC sampling to generate metamaterial designs satisfying target functional responses, producing broader design-space coverage than genetic algorithms on acoustic and optical test cases with fewer than 250 training samples.
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An adaptive vulnerability-aware fault tolerance framework for neural networks that employs a GNN predictor to dynamically adjust protection policies, achieving over 95% prediction accuracy and 42.12% average overhead reduction.
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Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning
RIGID uses a random forest forward model and MCMC sampling to generate metamaterial designs satisfying target functional responses, producing broader design-space coverage than genetic algorithms on acoustic and optical test cases with fewer than 250 training samples.
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Adaptive Soft Error Protection for Neural Network Processing
An adaptive vulnerability-aware fault tolerance framework for neural networks that employs a GNN predictor to dynamically adjust protection policies, achieving over 95% prediction accuracy and 42.12% average overhead reduction.