🤖 Digital Twin and Physics-Informed Machine Learning (DTP) Wing
Digital twin is a virtual copy of something real, like a machine, a building, or even a person, that helps us understand and test it without using the actual thing. For example, doctors can use a digital twin of a human heart to see how it reacts to different treatments, or engineers can use one to test how a plane performs in bad weather. Even in sports, a digital twin of an athlete can be made to improve their training! Digital twins are important because they let us test, predict, and improve real-world objects or systems without risking damage or failure. They can make manufacturing safer, improve medical treatments, and even help cities become more efficient. In the future, digital twins could be used to simulate entire smart cities, create personalized healthcare plans, and make energy systems more sustainable and resilient.
The Digital Twin and Physics-Informed Machine Learning (DT-PIML) Wing aims to bridge the gap between physical systems and their virtual counterparts, combining state-of-the-art simulation technologies with data-driven models. Digital Twin technology offers precise virtual replicas of real-world systems, allowing continuous monitoring, optimization, and predictive maintenance. By integrating physics-based principles with machine learning, this wing focuses on enhancing model accuracy and robustness, especially in complex industrial and engineering applications. Our goal :
- Develop advanced Digital Twin models to control, optimize, simulate and monitor different systems in real time.
- Develop advanced AI Agents to control, optimize, simulate and monitor different systems in real time.
- Incorporate physics-informed machine learning techniques to improve model reliability and performance in scenarios with limited or noisy data.
- Explore the application of Digital Twins in diverse sectors, such as smart manufacturing, energy systems, supply chains, and medical AI.
Wing Members
Publications
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Stages of Integrating Digital Twin in Fused Deposition Modelling
Abdur Rahman, Azmine Toushik Wasi, Mahfuz Ahmed Anik, and Md Manjurul Ahsan DigiTwin Conference | Purdue University â–ª Accepted â–ª Poster
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Sustainable Management of Rare Earth Elements for Clean Energy Using Prescriptive Digital Twins
Mahfuz Ahmed Anik , Iqramul Hoque , MD Shafikul Islam, Azmine Toushik Wasi, Md Manjurul Ahsan and Mahathir Mohammad Bappy DigiTwin Conference | Purdue University â–ª Accepted â–ª Poster