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Data-driven rogue waves and parameters discovery in nearly integrable <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2780" altimg="si47.svg"><mml:mrow><mml:mi mathvariant="script">P</mml:mi><mml:mi>T</mml:mi></mml:mrow></mml:math>-symmetric Gross–Pitaevskii equations via PINNs deep learning

Ming Zhong, Shibo Gong, Shou‐Fu Tian, Zhenya Yan

2022Physica D Nonlinear Phenomena47 citationsDOI

Topics & Concepts

Integrable systemInverse problemArtificial neural networkInverseAlgorithmFocus (optics)Rogue waveNonlinear systemApplied mathematicsComputer scienceMathematicsPhysicsArtificial intelligenceMathematical analysisQuantum mechanicsGeometryOpticsQuantum Mechanics and Non-Hermitian PhysicsNonlinear Waves and SolitonsNonlinear Photonic Systems
Data-driven rogue waves and parameters discovery in nearly integrable <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2780" altimg="si47.svg"><mml:mrow><mml:mi mathvariant="script">P</mml:mi><mml:mi>T</mml:mi></mml:mrow></mml:math>-symmetric Gross–Pitaevskii equations via PINNs deep learning | Litcius