4 papers
Domain-informed Adaptive Sampling for Generalizable PINNs in Metal Additive Manufacturing via Conditional Flow Matching
Hyeonsu Lee, Jihoon Jeong
Accurate thermal modeling is essential in metal additive manufacturing (AM) for understanding the process-structure-property chain. Physics-informed neural networks (PINNs) offer e…
Particle Inertia-Driven Pore Formation over Material Property Effects in Laser Powder-blown Directed Energy Deposition
Dong Hee Kang, Samantha Webster, Sampson Canacoo +3
Laser Powder-blown Directed Energy Deposition (LP-DED) offers flexibility for process and materials and high productivity (~5 kg/h), but process-induced pores often compromise mech…
Shared Emotion Geometry Across Small Language Models: A Cross-Architecture Study of Representation, Behavior, and Methodological Confounds
Jihoon Jeong
We extract 21-emotion vector sets from twelve small language models (six architectures x base/instruct, 1B-8B parameters) under a unified comprehension-mode pipeline at fp16 precis…
Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework
Hyeonsu Lee, Jihoon Jeong
Accurate temperature field prediction in metal additive manufacturing (AM) is essential for understanding the process-structure-performance relationship. While prior studies have e…