4 papers · 1 filter
Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations
Heechang Kim, Qianying Cao, Hyomin Shin +3
Neural operators have emerged as fast surrogate solvers for parametric partial differential equations (PDEs). However, purely data-driven models often require extensive training da…
Physics-Informed Machine Learning in Biomedical Science and Engineering
Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey +1
Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws wit…
Importance of localized dilatation and distensibility in identifying determinants of thoracic aortic aneurysm with neural operators
David S. Li, Somdatta Goswami, Qianying Cao +4
Thoracic aortic aneurysms (TAAs) arise from diverse mechanical and mechanobiological disruptions to the aortic wall that increase the risk of dissection or rupture. Evidence links…
Automatic selection of the best neural architecture for time series forecasting
Qianying Cao, Shanqing Liu, Alan John Varghese +3
Time series forecasting plays a pivotal role in a wide range of applications, including weather prediction, healthcare, structural health monitoring, predictive maintenance, energy…