1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CE2026
Differentiable FEM versus PINN for pavement layer inverse analysis: enforcing physics as hard or soft constraints
Yongjin Choi, Hyeonbin Moon, Seunghwa Ryu
Automatic-differentiation-based inverse analysis methods, including physics-informed neural networks (PINNs) and differentiable programming, have recently shown great promise due t…
cs.AI2025★ 1 cited
Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
Hugon Lee, Hyeonbin Moon, Junhyeong Lee +1
Artificial intelligence (AI) is reshaping inverse design in manufacturing, enabling high-performance discovery in materials, products, and processes. However, purely data-driven ap…