5 citations · 5 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…