Songming Liu

Zhejiang Shuren University, Zhejiang University

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About

Songming Liu is a rising researcher at the intersection of artificial intelligence and physical sciences, with a primary focus on physics-informed machine learning. His most influential work, the 2022 survey "Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications," has already garnered 90 citations, establishing him as a key voice in this rapidly evolving field. This comprehensive review synthesizes how data-driven machine learning—traditionally dominant in computer vision and reinforcement learning—can be integrated with governing physical laws to solve complex scientific and engineering problems. By bridging the gap between pure data-driven approaches and physics-based modeling, Liu's survey has become an essential resource for researchers seeking to develop more robust, interpretable, and physically consistent AI systems. Beyond this foundational contribution, Liu explores practical applications in robotics, including patient monitoring systems and path-following robots enhanced by neural networks. His work demonstrates a commitment to translating theoretical advances into real-world solutions, particularly in healthcare and autonomous systems. As physics-informed machine learning continues to reshape scientific computing, Liu's contributions position him at the forefront of this transformative research paradigm.