MATSE—multi-fidelity augmented time-series emulation: galvanic corrosion applications
Complex physio-chemical phenomena can be investigated using high-fidelity physics-based finite element (FE) models. These models provide accurate predictions of the response of interest, as well as insights into its evolution over time. However, these models are computationally expensive, limiting w...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/add23a |
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