5 citations · 5 across the 9 of their papers we have counts for
9 papers
TabPFN for Zero-shot Parametric Engineering Design Generation
Ke Wang, Yifan Tang, Nguyen Gia Hien Vu +2
Deep generative models for engineering design often require substantial computational cost, large training datasets, and extensive retraining when design requirements or datasets c…
RePaint-Enhanced Conditional Diffusion Model for Parametric Engineering Designs under Performance and Parameter Constraints
Ke Wang, Nguyen Gia Hien Vu, Yifan Tang +2
This paper presents a RePaint-enhanced framework that integrates a pre-trained performance-guided denoising diffusion probabilistic model (DDPM) for performance- and parameter-cons…
Adaptation and Fine-tuning with TabPFN for Travelling Salesman Problem
Nguyen Gia Hien Vu, Yifan Tang, Rey Lim +4
Tabular Prior-Data Fitted Network (TabPFN) is a foundation model designed for small to medium-sized tabular data, which has attracted much attention recently. This paper investigat…
Capturing Lifecycle System Degradation in Digital Twin Model Updating
Yifan Tang, Mostafa Rahmani Dehaghani, G. Gary Wang
Digital twin (DT) has emerged as a powerful tool to facilitate monitoring, control, and other decision-making tasks in real-world engineering systems. Online update methods have be…
Hybrid Metaheuristic Vehicle Routing Problem for Security Dispatch Operations
Nguyen Gia Hien Vu, Yifan Tang, Rey Lim +1
This paper investigates the optimization of the Vehicle Routing Problem for Security Dispatch (VRPSD). VRPSD focuses on security and patrolling applications which involve challengi…
Selecting Subsets of Source Data for Transfer Learning with Applications in Metal Additive Manufacturing
Yifan Tang, M. Rahmani Dehaghani, Pouyan Sajadi +1
Considering data insufficiency in metal additive manufacturing (AM), transfer learning (TL) has been adopted to extract knowledge from source domains (e.g., completed printings) to…
Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks
Pouyan Sajadi, Mostafa Rahmani Dehaghani, Yifan Tang +1
Accurately predicting the temperature field in metal additive manufacturing (AM) processes is critical to preventing overheating, adjusting process parameters, and ensuring process…
Online Two-stage Thermal History Prediction Method for Metal Additive Manufacturing of Thin Walls
Yifan Tang, M. Rahmani Dehaghani, Pouyan Sajadi +6
This paper aims to propose an online two-stage thermal history prediction method, which could be integrated into a metal AM process for performance control. Based on the similarity…
System identification and closed-loop control of laser hot-wire directed energy deposition using the parameter-signature-property modeling scheme
M. Rahmani Dehaghani, Atieh Sahraeidolatkhaneh, Morgan Nilsen +4
Hot-wire directed energy deposition using a laser beam (DED-LB/w) is a method of metal additive manufacturing (AM) that has benefits of high material utilization and deposition rat…