用主动学习提升神经网络逆向设计效率，少样本更准确。
AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems
- 结合主动学习与串联神经网络，智能挑选关键数据点
- 仅用更少训练样本，逆向设计精度显著优于传统方法
- 适合高维复杂设计问题，如气动翼型与光子结构优化
科学与工程中的逆向设计旨在确定实现特定性能目标的最优设计参数，但设计空间复杂且高维，常导致高昂计算成本。为此，我们提出一种新型混合方法，将主动学习与串联神经网络相结合，以提升逆向设计的效率与效果。主动学习可有选择地采样最具信息量的数据点，减少所需数据集规模而不牺牲准确性。我们在三个基准问题上验证该方法：机翼逆向设计、光子表面逆向设计以及扩散型偏微分方程的标量边界条件重构。结果表明，将主动学习融入串联神经网络，在所有测试任务中均优于标准方法，以更少的训练样本实现更高精度。
原文摘要 · Abstract (English)
Inverse design in science and engineering involves determining optimal design parameters that achieve desired performance outcomes, a process often hindered by the complexity and high dimensionality of design spaces, leading to significant computational costs. To tackle this challenge, we propose a novel hybrid approach that combines active learning with Tandem Neural Networks to enhance the efficiency and effectiveness of solving inverse design problems. Active learning allows to selectively sample the most informative data points, reducing the required dataset size without compromising accuracy. We investigate this approach using three benchmark problems: airfoil inverse design, photonic surface inverse design, and scalar boundary condition reconstruction in diffusion partial differential equations. We demonstrate that integrating active learning with Tandem Neural Networks outperforms standard approaches across the benchmark suite, achieving better accuracy with fewer training samples.
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