8 papers
Robust and Explainable 3D Mode Shape Recognition Using Region-Aware Graph Neural Networks
Tong Duy Son, Marc Brughmans, Andrey Hense +4
Mode shape recognition is a fundamental task in automotive NVH development, yet it remains dependent on manual visual inspection by experienced engineers. Existing approaches based…
Automotive Engineering-Centric Agentic AI Workflow Framework
Tong Duy Son, Zhihao Liu, Piero Brigida +4
Engineering workflows such as design optimization, simulation-based diagnosis, control tuning, and model-based systems engineering (MBSE) are iterative, constraint-driven, and shap…
Toward Generalizable Graph Learning for 3D Engineering AI: Explainable Workflows for CAE Mode Shape Classification and CFD Field Prediction
Tong Duy Son, Kohta Sugiura, Marc Brughmans +7
Automotive engineering development increasingly relies on heterogeneous 3D data, including finite element (FE) models, body-in-white (BiW) representations, CAD geometry, and CFD me…
ExAMPC: the Data-Driven Explainable and Approximate NMPC with Physical Insights
Jean Pierre Allamaa, Panagiotis Patrinos, Tong Duy Son
Amidst the surge in the use of Artificial Intelligence (AI) for control purposes, classical and model-based control methods maintain their popularity due to their transparency and…
Imitation Learning from Observations: An Autoregressive Mixture of Experts Approach
Renzi Wang, Flavia Sofia Acerbo, Tong Duy Son +1
This paper presents a novel approach to imitation learning from observations, where an autoregressive mixture of experts model is deployed to fit the underlying policy. The paramet…
Driving from Vision through Differentiable Optimal Control
Flavia Sofia Acerbo, Jan Swevers, Tinne Tuytelaars +1
This paper proposes DriViDOC: a framework for Driving from Vision through Differentiable Optimal Control, and its application to learn autonomous driving controllers from human dem…