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stat.ML2025
Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry
Pucheng Tang, Hongqiao Wang, Wenzhou Lin +2
Parametric partial differential equations (PDEs) serve as fundamental mathematical tools for modeling complex physical phenomena, yet repeated high-fidelity numerical simulations a…
cs.AI2025
DeePoly: A High-Order Accuracy Scientific Machine Learning Framework for Function Approximation and Solving PDEs
Li Liu, Heng Yong
Recently, machine learning methods have gained significant traction in scientific computing, particularly for solving Partial Differential Equations (PDEs). However, methods based…
cs.LG2025
A general physics-constrained method for the modelling of equation's closure terms with sparse data
Tian Chen, Shengping Liu, Li Liu +1
Accurate modeling of closure terms is a critical challenge in engineering and scientific research, particularly when data is sparse (scarse or incomplete), making widely applicable…