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cs.LG2025
Quasi-Random Physics-informed Neural Networks
Tianchi Yu, Ivan Oseledets
Physics-informed neural networks have shown promise in solving partial differential equations (PDEs) by integrating physical constraints into neural network training, but their per…
cs.LG2024★ 3 cited
Sinc Kolmogorov-Arnold network and its application for solving PDEs with singularities
Tianchi Yu, Jingwei Qiu, Jiang Yang +1
In this paper, we propose to use Sinc interpolation in the context of Kolmogorov-Arnold Networks, neural networks with learnable activation functions, which recently gained attenti…
cs.LG2024★ 3 cited
Spectral Informed Neural Network: An Efficient and Low-Memory PINN
Tianchi Yu, Yiming Qi, Ivan Oseledets +1
With growing investigations into solving partial differential equations by physics-informed neural networks (PINNs), more accurate and efficient PINNs are required to meet the prac…