核心概念

ZCS simplifies AD for physics-informed operator learning, reducing memory and time while maintaining training results.

要約

The content introduces the Zero Coordinate Shift (ZCS) algorithm for automatic differentiation in physics-informed operator learning. ZCS significantly reduces GPU memory and wall time for training DeepONets by simplifying derivatives calculation. The algorithm is compared to traditional methods like FuncLoop and DataVect across various PDE problems, showcasing its efficiency and effectiveness.

- ZCS introduces a novel approach to conduct automatic differentiation for physics-informed operator learning.
- The algorithm simplifies derivative calculations by introducing zero-valued dummy variables.
- ZCS significantly reduces memory consumption and training time compared to traditional methods.
- Experiments on reaction-diffusion, Burgers' equation, Kirchhoff-Love plates, and Stokes flow demonstrate the effectiveness of ZCS.
- Limitations include challenges with structured grid-based models like CNNs and FNOs.

他の言語に翻訳

原文コンテンツから

arxiv.org

統計

ZCSは、GPUメモリとウォールタイムを劇的に削減します。
ZCSは、自動微分のための新しいアプローチを導入します。

引用

"ZCS turns out to be one-order-of-magnitude more efficient in both memory and time."
"ZCS emerges as a replacement for both, with an outstanding superiority across all problem scales."

深掘り質問

ZCS stands out from other optimization techniques in machine learning by its ability to significantly reduce GPU memory consumption and training time for physics-informed operator learning. Unlike traditional methods like finite difference or analytical differentiation, ZCS simplifies the computation of high-order derivatives by introducing a zero-valued dummy scalar, leading to more efficient reverse-mode automatic differentiation. This reduction in memory usage and training time is crucial for complex problems involving high-dimensional data and functions.

The implications of ZCS on the future development of physics-informed machine learning are profound. By addressing the challenges associated with computing derivatives in pointwise operators, ZCS opens up new possibilities for efficiently training neural networks to solve partial differential equations (PDEs) without compromising accuracy or model performance. The significant improvements in memory efficiency and training speed offered by ZCS can pave the way for tackling larger-scale and more complex physics-based problems using deep learning models.

To adapt ZCS for use in structured grid-based models like Convolutional Neural Networks (CNNs), certain considerations need to be taken into account. While CNNs inherently satisfy translation invariance conditions required for AD, incorporating ZCS can further optimize memory usage and computational efficiency by feeding two scalars instead of full coordinates as leaf variables during backpropagation. By leveraging ZCS within CNN architectures on structured grids, researchers can enhance the performance of physics-informed machine learning models while maintaining compatibility with grid-based data structures.

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