  During the curing process of composites, the temperature history heavily
determines the evolutions of the field of degree of cure as well as the
residual stress, which will further influence the mechanical properties of
composite, thus it is important to simulate the real temperature history to
optimize the curing process of composites. Since thermochemical analysis using
Finite Element (FE) simulations requires heavy computational loads and
data-driven approaches suffer from the complexity of highdimensional mapping.
This paper proposes a Residual Fourier Neural Operator (ResFNO) to establish
the direct high-dimensional mapping from any given cure cycle to the
corresponding temperature histories. By integrating domain knowledge into a
time-resolution independent parameterized neural network, the mapping between
cure cycles to temperature histories can be learned using limited number of
labelled data. Besides, a novel Fourier residual mapping is designed based on
mode decomposition to accelerate the training and boost the performance
significantly. Several cases are carried out to evaluate the superior
performance and generalizability of the proposed method comprehensively.
