Physics-Guided Scientific Machine Learning for Trustworthy Mechanical and Physical Systems

Successful applicants will join VinUniversity as Master’s students / PhD students at the College of Engineering and Computer Science (CECS), VinUniversity

Project Information 

In modern engineering domains such as robotics, smart materials, and industrial machinery, accurately modeling and predicting system behavior is critical to ensuring performance, safety, and reliability. While machine learning has achieved significant breakthroughs in data-driven analysis, purely data-based models often fail to fully capture the underlying physical laws governing these systems, which may limit their reliability when deployed in real-world environments. 

his project aims to develop a new generation of Scientific Machine Learning models in which physical laws are directly embedded into AI architectures. This approach seeks to bridge the rigor of scientific theory with the adaptability of modern artificial intelligence. 

The research focuses on applications in robotics, computational mechanics, materials engineering, and industrial systems – areas that demand high standards of operational safety and energy efficiency.

Principal Investigator: Asst. Prof. Nguyen Vu Linh

Research Objectives 

The project is structured around the following key objectives: 

(i) Design and develop machine learning models that integrate physical laws, ensuring physical consistency and improved generalization compared to purely data-driven approaches. 

(ii) Investigate methodologies that combine differential equations, computational mechanics, and deep neural networks to enhance predictive accuracy for complex mechanical and physical systems. 

(iii) Establish a comprehensive framework for evaluating trustworthiness, including model interpretability, stability, and safety in real-world deployment. 

(iv) Validate and apply the proposed models to practical problems in robotics, materials, and industrial machinery, with the goal of optimizing energy efficiency and operational safety.

Benefits and Impact 

The project is expected to make significant contributions to the field of trustworthy AI and advanced computational engineering. By embedding physical laws, differential equations, and conservation principles directly into machine learning architectures, the research aims to develop AI systems that achieve high predictive accuracy while ensuring physical consistency, interpretability, and computational efficiency. 

Strategically, the project contributes to advancing Industry 4.0 initiatives in Vietnam and reinforces VinUni’s leadership in physics-guided AI and trustworthy engineering systems research. 

Project Contact 

For further information, please contact Asst. Prof. Nguyen Vu Linh via email [email protected].

Graduate Admissions Contact 

• VinUni Graduate Admissions
•  0978 549 846
• [email protected]

Apply now: https://apply.vinuni.edu.vn/graduate/s/login/ 

#VinUni #VinUniversity#VinUniGraduateResearchExcellenceProgram #VinUniPhD #VinUniMasters#VinUniResearchProjects