Waqas Waheed

Khalifa University of Science and Technology

Papers

Total Citations

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About

Waqas Waheed is a rising force in computational mechanics and advanced manufacturing, whose work bridges artificial intelligence and materials science. His primary research focuses on the mechanical behavior of triply periodic minimal surfaces (TPMS)—intricate, bio-inspired lattice structures with transformative potential in robotics, biomedical implants, and impact energy absorption. His most-cited paper, “A Deep Artificial Neural Network Model for Predicting the Mechanical Behavior of Triply Periodic Minimal Surfaces under Damage Loading” (2024, 7 citations), introduces a pioneering deep learning framework that accurately forecasts how these complex architectures deform and fail under damage conditions. This contribution is significant because it replaces costly, time-consuming physical testing with rapid, data-driven predictions, accelerating the design of lightweight, high-performance components. By integrating neural networks with structural mechanics, Waheed provides engineers a powerful tool to optimize TPMS for real-world applications—from crash-resistant automotive parts to patient-specific orthopedic scaffolds. Though early in his career, his work has already garnered attention for its novelty and practical impact, positioning him as a key innovator at the intersection of artificial intelligence and mechanical design.