2 papers
math.NA2025
An Efficient Deep Learning Approach for Approximating Parameter-to-Solution Maps of PDEs
Guanhang Lei, Zhen Lei, Lei Shi +1
In this paper, we consider approximating the parameter-to-solution maps of parametric partial differential equations (PPDEs) using deep neural networks (DNNs). We propose an effici…
math.NA2025
Point Cloud Neural Operator for Parametric PDEs on Complex and Variable Geometries
Chenyu Zeng, Yanshu Zhang, Jiayi Zhou +5
Surrogate models are critical for accelerating computationally expensive simulations in science and engineering, particularly for solving parametric partial differential equations…