  Woven fabrics play an essential role in everyday textiles for
clothing/sportswear, water filtration, and retaining walls, to reinforcements
in stiff composites for lightweight structures like aerospace, sporting,
automotive, and marine industries. Several possible combinations of weave
patterns and material choices, which comprise weave architecture, present a
challenging question about how they could influence the physical and mechanical
properties of woven fabrics and reinforced structures. In this paper, we
present a novel Physics-Constrained Neural Network (PCNN) to predict the
mechanical properties like the modulus of weave architectures and the inverse
problem of predicting pattern/material sequence for a design/target modulus
value. The inverse problem is particularly challenging as it usually requires
many iterations to find the appropriate architecture using traditional
optimization approaches. We show that the proposed PCNN can effectively predict
weave architecture for the desired modulus with higher accuracy than several
baseline models considered. We present a feature-based optimization strategy to
improve the predictions using features in the Grey Level Co-occurrence Matrix
(GLCM) space. We combine PCNN with this feature-based optimization to discover
near-optimal weave architectures to facilitate the initial design of weave
architecture. The proposed frameworks will primarily enable the woven composite
analysis and optimization process, and be a starting point to introduce
Knowledge-guided Neural Networks into the complex structural analysis.
