Joseph Leonor

Scientific Computing • Machine Learning • Additive Manufacturing

Joseph Leonor

About

PhD candidate in Mechanical Engineering at Northwestern University focused on HPC, GPU acceleration, numerical simulation, and machine learning. Creator of GO-MELT, a GPU-accelerated thermal simulation framework delivering a 678x speedup for metal additive manufacturing. My work bridges physics-based modeling, surrogate modeling, and workflow automation to build scalable, reproducible tools for scientific and engineering applications.

Research

GO-MELT: Multi-level finite element framework

GO-MELT integrates GPU-optimized multilevel FEM to efficiently resolve melt-pool and part-scale thermal behavior across complex AM geometries.

HASTE: Data-driven machine learning coupling to numerical solvers

HASTE integrates data-driven surrogate models into GO-MELT, enabling seamless switching between FEM and ML predictions during near-steady-state melt pool evolution.

Publications

Contact

LinkedIn: linkedin.com/in/joseph-p-leonor