Emerging technologies have a profound potential to address several challenges facing our world, such as climate change, energy consumption, healthcare, and societal welfare. However, the design and implementation of these technologies require a deep understanding of the principles of physics. Simultaneously, the technologies should be robust, efficient, and adaptable to diverse scenarios and demands. Despite significant strides in technology development, creating systems that leverage physics principles to drive sustainable and beneficial outcomes remains a challenge.
Capitalising on our broad expertise in physics, engineering, machine learning, and social sciences, we propose to explore and create physics-informed technologies designed for the greater good. This involves harnessing the principles of quantum mechanics, thermodynamics, electrodynamics, and more to foster the development of technologies such as renewable energy systems, advanced healthcare devices, or sustainable transportation solutions. We strive to develop a cross-disciplinary framework that combines advanced machine learning algorithms with physics-based models. The incorporation of physics principles into machine learning will facilitate more accurate predictions, deeper insights, and robust control systems, leading to the development of more efficient and sustainable technologies.