Transfer learning from low-fidelity to high-fidelity surrogate models enables accurate nonlinear time-history analysis predictions for complex structures with only 20 training samples.
Artificial neural network application in predicting prob- abilistic seismic demands of bridge components
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Transfer Learning-Based Surrogate Modeling for Nonlinear Time-History Response Analysis of High-Fidelity Structural Models
Transfer learning from low-fidelity to high-fidelity surrogate models enables accurate nonlinear time-history analysis predictions for complex structures with only 20 training samples.