  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.
