most citedPredicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers via the modified PINN

86 citations · 187 across the 6 of their papers we have counts for

collaborators

6 papers

nlin.PS202232 cited

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…

nlin.PS202258 cited

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…

physics.optics20211 cited

Generation and dynamics of soliton and soliton molecules from a VSe2/GO-based fiber laser

Benhai Wang, Haobin Han, Lijun Yu +2

Recently, in addition to exploring the application of new saturable absorber devices in fiber lasers, soliton dynamics has also become a focus of current research. In this article,…

nlin.PS202186 cited

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…

nlin.PS20212 cited

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…

nlin.PS20218 cited

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…