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- W4285807746 abstract "Like many specialty applications, the pace of designing structures for impact protection is limited by its reliance on specialized testing. Here, we develop a transfer learning approach to determine how more widely available quasi-static testing can be used to predict impact protection. We first extensively test a parametric family of lattices in both impact and quasi-static domains and train a model that predicts impact performance to within 8% using only quasi-static measurements. Next, we test the transferability of this model using a distinct family of lattices and find that performance rank was well predicted even for structures whose behavior extrapolated beyond the training set. Finally, we combine 812 quasi-static and 141 impact tests to train a model that predicts absolute impact performance of novel lattices with 18% error. These results highlight a path for accelerating design for specialty applications and that transferrable mechanical insight can be obtained in a data-driven manner." @default.
- W4285807746 created "2022-07-19" @default.
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- W4285807746 date "2022-09-01" @default.
- W4285807746 modified "2023-10-03" @default.
- W4285807746 title "Designing lattices for impact protection using transfer learning" @default.
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- W4285807746 doi "https://doi.org/10.1016/j.matt.2022.06.051" @default.
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