24 citations · 25 across the 9 of their papers we have counts for
9 papers
A Hybrid GNN-FEM Framework for Phase-Field Fracture Simulation. Physics-Preserving Hybridization for Generalizable Surrogate Modeling
Hyeonbin Moon, Yongjin Choi, Seunghwa Ryu
Scientific machine learning (SciML) has emerged as a promising approach for accelerating simulations of complex physical systems, yet achieving physically consistent and generaliza…
Physics-Informed Discovery of Yield Functions in Plasticity via Convex Neural Representations
Hyeonbin Moon, Donghyuk Cho, Jecheon Yu +2
Identifying anisotropic yield functions remains challenging since yielding is not directly observed in full-field mechanical measurements, directional calibration can require many…
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…
Real-Time Structural Health Monitoring with Bayesian Neural Networks: Distinguishing Aleatoric and Epistemic Uncertainty for Digital Twin Frameworks
Hanbin Cho, Jecheon Yu, Hyeonbin Moon +5
Reliable real-time analysis of sensor data is essential for structural health monitoring (SHM) of high-value assets, yet a major challenge is to obtain spatially resolved full-fiel…
Thermal Conductivity Estimation of Thermoelectric Materials with Uncertainty Quantification Using Bayesian Physics-Informed Neural Networks
Hyeonbin Moon, Hanbin Cho, Wabi Demeke +2
Characterizing the temperature-dependent thermal conductivity is challenging because the property varies strongly with temperature and reliable heat flow measurement, not just temp…
Physics-informed neural network framework for solving forward and inverse flexoelectric problems
Hyeonbin Moon, Donggeun Park, Jinwook Yeo +1
Flexoelectricity, the coupling between strain gradients and electric polarization, poses significant computational challenges due to its governing fourth-order partial differential…
Physics-Informed Neural Operators for Generalizable and Label-Free Inference of Temperature-Dependent Thermoelectric Properties
Hyeonbin Moon, Songho Lee, Wabi Demeke +2
Accurate characterization of temperature-dependent thermoelectric properties (TEPs), such as thermal conductivity and the Seebeck coefficient, is essential for reliable modeling an…
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…
Physics-Informed Neural Network-Based Discovery of Hyperelastic Constitutive Models from Extremely Scarce Data
Hyeonbin Moon, Donggeun Park, Hanbin Cho +3
The discovery of constitutive models for hyperelastic materials is essential yet challenging due to their nonlinear behavior and the limited availability of experimental data. Trad…