144 citations · 331 across the 7 of their papers we have counts for
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Predicting nonlinear dynamics of optical solitons in optical fiber via the SCPINN
Yin Fang, Wen-Bo Bo, Ru-Ru Wang +2
The strongly-constrained physics-informed neural network (SCPINN) is proposed by adding the information of compound derivative embedded into the soft-constraint of physics-informed…
Data-driven soliton solutions and model parameters of nonlinear wave models via the conservation-law constrained neural network method
Yin Fang, Gang-Zhou Wu, Yue-Yue Wang +1
In the process of the deep learning, we integrate more integrable information of nonlinear wave models, such as the conservation law obtained from the integrable theory, into the n…
Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers via the modified PINN
Gang-Zhou Wu, Yin Fang, Yue-Yue Wang +2
A modified physics-informed neural network is used to predict the dynamics of optical pulses including one-soliton, two-soliton, and rogue wave based on the coupled nonlinear Schrö…
Modified physics-informed neural network method based on the conservation law constraint and its prediction of optical solitons
Gang-Zhou Wu, Yin Fang, Yue-Yue Wang +1
Based on conservation laws as one of the important integrable properties of nonlinear physical models, we design a modified physics-informed neural network method based on the cons…
Data-driven femtosecond optical soliton excitations and parameters discovery of the high-order NLSE using the PINN
Yin Fang, Gang-Zhou Wu, Yue-Yue Wang +1
We use the physics-informed neural network to solve a variety of femtosecond optical soliton solutions of the high order nonlinear Schrödinger equation, including one-soliton solut…