Professor Faruque Hasan: Energy Innovators @ the Texas A&M Energy Institute

Описание: 📖 The Texas A&M Energy Institute's new book, "Energy Innovators,” by Tim Gregg, is now available for purchase from Amazon or Barnes & Noble. 📖

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The Texas A&M Energy Institute has produced the Energy Innovators Video Series, which highlights the successes of the research and scholarship of the people of the Texas A&M Energy Institute.

This video features Professor Faruque Hasan, a mathematical modeler dedicated to designing and optimizing complex energy systems. In this episode of Energy Innovators, Dr. Hasan explains why Texas—with its massive demand and diverse portfolio of oil, gas, coal, and renewables—is the ultimate real-world test bed for energy research.

Dr. Hasan breaks down how his team builds "digital twins" to safely and efficiently simulate the future of our energy systems. He also offers a critical look at the limitations of modern AI and machine learning, explaining why we must combine raw data with rigorous physical sciences to build a resilient, sustainable energy future.

In this video, we cover:

The Ultimate Energy Test Bed: Why Texas's unique combination of every major energy component makes it the perfect environment for systems design and optimization.

Building Digital Twins: How mathematical models representing complex systems (from electrons and hydrogen molecules to power plants and transmission lines) help researchers test future scenarios without risking real-world infrastructure.

The Limits of AI and Machine Learning: Why tools relying solely on existing data and statistical correlations fall short when predicting unprecedented events or new emergent behaviors in energy grids.

Physics-Informed Neural Networks: How Dr. Hasan’s research group bridges the gap by combining machine learning with strict domain knowledge—ensuring AI models actually obey the laws of physics and chemistry.

The Systems Thinking Approach: How the Texas A&M Energy Institute applies a big-picture mindset to design real-world applications that make global energy systems more secure, resilient, and sustainable.

"Chat GPT, or AI, or machine learning models give us speed in terms of analyzing data because these are all statistical correlations... But, if you are interested in understanding new phenomena and emergent behavior that we may have not seen before, we do not have any data to describe that. Then, existing machine learning models or AI will not give you correct answer... that's where we need to combine existing data with existing knowledge." — Professor Faruque Hasan

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About the Texas A&M Energy Institute:
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The Texas A&M Energy Institute is a joint institute between Texas A&M University and the Texas A&M Engineering Experiment Station (TEES). The institute engages undergraduate and graduate students, postdoctoral associates, research staff, and faculty members in the study and development of innovative technologies and policies for energy production and energy conservation during energy evolution. Special attention is paid to elucidating the complexity among the interacting components of energy, economics, law, public policy, and the environment.

The institute is the home of innovative educational programs, including the 10-month Master of Science in Energy and graduate-level Certificate in Energy, that target both students and professionals who want to be educated on the high-impact and interdisciplinary facets of the energy research landscape through quantitative analytical methods and multi-scale systems based approaches.

With approximately 300 faculty affiliates from a wide array of academic units, including nine colleges and schools at TAMU, more than 20 TAMU departments, two TAMU branch campuses, and six TAMUS member institutions, along with a unique community of more than 450 doctoral students and postdoctoral fellows in the Texas A&M Energy Research Society, cutting-edge solutions are being realized through true interdisciplinary collaborations that will address the complexity and challenges of the world’s energy future.