• 學術發表

• PINNs

• Linear Elasticity

physics-based modeling of instruments often relies on the FEM. However, it is computationally expensive and not flexible. This research proposes a neural network-based surrogate model for predicting the full-field displacement distribution and eigenfrequencies of violin plates. The proposed method does not require high-end GPUs and exhibits strong generalization capabilities and computational efficiency.

• 學術著作

• 提琴聲學

• Neural Operator

• Deep Learning

• Unsupervised Timbre Analysis

• CNN/LSTM