Rapid mechanical prediction of woven ceramic fabrics via a neural network surrogate model based on the parameterized unit cell

Key Points

• Mechanical prediction accurately models woven ceramic fabrics using a neural network, enabling faster design processes.
• The surrogate model achieves predictions based on parameterized unit cell characteristics, making it efficient and effective.
• By analyzing diverse unit cells, the study enhances understanding of material behavior under various conditions.
• This approach supports quicker prototyping of woven ceramics, potentially speeding up innovation in material applications.