Application of Multi-Fidelity Transfer Learning with Autoencoders for Efficient Construction of Surrogate Models
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This talk presents a multi-fidelity transfer learning approach using autoencoders to reduce the cost of constructing surrogate models, leveraging inexpensive low-fidelity data to pre-train models that are then refined with a limited amount of high-fidelity data.