Deep Operator Networks (DeepONets) and their physics-informed variants have
shown significant promise in learning mappings between function spaces of
partial differential equations, enhancing the generalization of traditional
neural networks. However, for highly nonlinear real-world applications like
aerospace composites processing, existing models often fail to capture
underlying solutions accurately and are typically limited to single input
functions, constraining rapid process design development. This paper introduces
an advanced physics-informed DeepONet tailored for such complex systems with
multiple input functions. Equipped with architectural enhancements like
nonlinear decoders and effective training strategies such as curriculum
learning and domain decomposition, the proposed model handles high-dimensional
design spaces with significantly improved accuracy, outperforming the vanilla
physics-informed DeepONet by two orders of magnitude. Its zero-shot prediction
capability across a broad design space makes it a powerful tool for
accelerating composites process design and optimization, with potential
applications in other engineering fields characterized by strong nonlinearity.

该论文介绍了一种针对复杂系统和多输入函数的先进物理信息 DeepONet 模型，通过引入非线性解码器、课程学习和域分解等架构增强和有效训练策略，该模型在高维设计空间中具有显著提高的准确性，比基本物理信息 DeepONet 模型提升了两个数量级。它在广泛的设计空间中具有零样本预测能力，成为加速复合材料工艺设计和优化的强大工具，在其他工程领域具有强非线性特征的应用具有潜在的应用价值。

一个高级的受物理启发的神经算子用于高度非线性系统的全面设计优化：航空航天复合材料加工案例研究

An Advanced Physics-Informed Neural Operator for Comprehensive Design  Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing  Case Study