Ada2MF: Dual-adaptive multi-fidelity neural network approach and its application in wind turbine wake prediction

In the context of data-driven deep learning, employing multi-fidelity methods for swift and precise wake field prediction is a novel attempt. Current Multi-Fidelity Neural Networks (MFNNs) face accuracy loss due to structure theory limitations, and performance degradation from negative transfer. To address these issues and enhance wake prediction performance, we propose a Dual-Adaptive Multi-Fidelity Neural Network (Ada2MF) 2 MF) framework. This framework features an adaptive multi-fidelity (AMF) module that integrates three subnetworks via a learnable weighted gate, effectively capturing the linear, nonlinear, and residual characteristics of high-fidelity data and mitigating theoretical accuracy loss. Additionally, the adaptive fast weighting (AFW) module employs a dynamic loss-weighting algorithm to optimally balance multi-fidelity losses and prevent negative transfer. Initial validation on benchmark functions and further evaluation using a multi-fidelity wind turbine wake field database confirm the effectiveness of Ada2MF. 2 MF. Specifically, with a complete dataset, Ada2MF 2 MF achieves 66% and 41% improvements in wake prediction accuracy over single-fidelity neural networks and MFNN, respectively. Even with an 80% reduction in data volume, these improvements escalate to 82% and 78%, without incurring significant accuracy loss. Such results underscore Ada2MF's 2 MF's remarkable ability to improve prediction accuracy while substantially reducing the dependency on high-fidelity data.