SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition

arXiv:2211.08760v2 Announce Type: replace
Abstract: Physics-informed neural networks (PINNs) have attracted significant attention for solving partial differential equations (PDEs) in recent years because they alleviate the curse of dimensionality that appears in traditional methods. However, the most disadvantage of PINNs is that one neural network corresponds to one PDE. In practice, we usually need to solve a class of PDEs, not just one. With the explosive growth of deep learning, many useful techniques in general deep learning tasks are also …

abstract arxiv attention cs.lg cs.na differential dimensionality however math.na network networks neural network neural networks physics physics-informed singular svd the curse of dimensionality transfer transfer learning type value via