Bio

Jake Peloquin is an assistant professor in the Edward P. Fitts Department of Industrial and Systems Engineering at NC State University. His research explores faster ways to design and manufacture advanced materials. In particular, he uses AI-driven optimization and multiscale analysis.

He combines mechanical testing, computer modeling, and data analytics to study structural variability. For example, his work examines both hierarchical materials and those created through additive manufacturing, such as lattices and multifunctional composites. Moreover, he seeks to bridge materials science, advanced manufacturing, and data-driven design.

As a result, Peloquin develops predictive frameworks that improve performance, reliability, and manufacturability. His work supports fields like aerospace, energy, and biomedical systems. In addition, he aims to create adaptable AI models. These models can learn from limited data and transfer across processes, materials, or geometries. Ultimately, his goal is to shorten deployment time.

Area(s) of Expertise

Peloquin focuses his research on advanced manufacturing, where he studies new methods to make materials more efficient. In addition, he applies artificial intelligence to improve design and production. He also investigates materials science to understand how different structures and properties influence performance. Furthermore, he examines structural mechanics to see how materials respond under stress and force. Finally, he explores multiscale mechanics, which connects small-scale behaviors to larger systems.

Publications

• Predicting compressive stress-strain behavior of elasto-plastic porous media via morphology-informed neural networks , Communications Engineering (2025)
• Structure-performance relationships of multi-material jetting polymeric composites designed at the voxel scale: Distribution and composition effects , Journal of Manufacturing Processes (2024)
• Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning , Materials & Design (2023)
• Printability and mechanical behavior as a function of base material, structure, and a wide range of porosities for polymer lattice structures fabricated by vat-based 3D printing , Additive manufacturing (2023)
• Tensile performance data of 3D printed photopolymer gyroid lattices , Data in Brief (2023)

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