5 papers
KernelOnet: An Interpretable Neural Operator Based on Kernel Functions
Yuan Guo, Hanshu Chen, Qiang Xi +2
This paper proposes an interpretable neural operator framework, the Kernel Operator Network (KernelOnet), which incorporates kernel functions explicitly into the neural operator ar…
PIKFNO: An Interpretable Neural Operator Based on Physics Informed Kernel Function
Yuan Guo, Hanshu Chen, Zhuojia Fu
This work proposes a new interpretable neural operator framework, termed the Physics Informed Kernel Function Neural Operator (PIKFNO), which explicitly incorporates physics inform…
Mapping-based Hard-constrained Physics-Informed Neural Networks for unbounded wave problems
Tao Zhang, Hanshu Chen, Ilia Marchevsky +1
The aim of this paper is to introduce a Mapping-based Hard-constrained Physics-Informed Neural Network (MH-PINN) for efficiently and accurately solving unbounded wave problems. Fir…
Virtual boundary integral neural network for three-dimensional exterior acoustic problems
Jiahao Li, Qiang Xi, Ilia Marchevskiy +1
This paper presents a virtual boundary integral neural network (VBINN) for exterior acoustic problems in three dimensions. The method introduces a virtual boundary inside the scatt…
Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations
Hongquan Wang, Hanshu Chen, Ilia Marchevsky +1
DeepONet enables retraining-free inference across varying initial conditions or source terms at the cost of high computational requirements. This paper proposes a hybrid quantum op…