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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
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
Research article (Physica D Nonlinear Phenomena, 2022) · cited 47× · AI/ML
Data-driven rogue waves and parameters discovery in nearly integrable PT-symmetric Gross–Pitaevskii equations via PINNs deep learning
Summary
Data-driven rogue waves and parameters discovery in nearly integrable PT-symmetric Gross–Pitaevskii equations via PINNs deep learning is a scholarly article<sup id="cite-A2" class="cite-ref" title="Data-driven rogue waves and parameters discovery in nearly integrable [1].
Key Facts
Data-driven rogue waves and parameters discovery in nearly integrable PT-symmetric Gross–Pitaevskii equations via PINNs deep learning's instance of is recorded as scholarly article<sup id="cite-C1" class="cite-ref" title="Data-driven rogue waves and parameters discovery in nearly integrable [2].
References
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APA4ort.xyz Knowledge Graph. (2026). 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. Retrieved May 24, 2026, from https://4ort.xyz/entity/data-driven-rogue-waves-and-parameters-discovery-in-nearly-integrable-mml-math-xmlns-mml-http-www-w3-org-1998-math-mathm
MLA“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.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/data-driven-rogue-waves-and-parameters-discovery-in-nearly-integrable-mml-math-xmlns-mml-http-www-w3-org-1998-math-mathm.
BibTeX@misc{4ortxyz_data-driven-rogue-waves-and-parameters-discovery-in-nearly-integrable-mml-math-xmlns-mml-http-www-w3-org-1998-math-mathm_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{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}}, year = {2026}, url = {https://4ort.xyz/entity/data-driven-rogue-waves-and-parameters-discovery-in-nearly-integrable-mml-math-xmlns-mml-http-www-w3-org-1998-math-mathm}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): 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 — https://4ort.xyz/entity/data-driven-rogue-waves-and-parameters-discovery-in-nearly-integrable-mml-math-xmlns-mml-http-www-w3-org-1998-math-mathm (retrieved 2026-05-24)
