دانشگاه گیلان
معاونت پژوهش و فناوری دانشگاه گیلان
انتشارات دانشگاه گیلان

 آمار

تعداد نشریات32
تعداد شماره‌ها865
تعداد مقالات8,401
تعداد مشاهده مقاله53,345,063
تعداد دریافت فایل اصل مقاله9,533,569
Shahul, Mohamed, Gunasekar, Tharmalingam, SANTRA, SHYAM, Baleanu, Dumitru, Yildirim, Yakup. (1405). A deep learning PINN-LSTM fusion framework for solving ordinary differential equations from real-life systems. فیزیولوژی و بیوتکنولوژی آبزیان, (), -. doi: 10.22124/jmm.2026.32516.2949
Mohamed Ashik Shahul; Tharmalingam Gunasekar; SHYAM SUNDAR SANTRA; Dumitru Baleanu; Yakup Yildirim. "A deep learning PINN-LSTM fusion framework for solving ordinary differential equations from real-life systems". فیزیولوژی و بیوتکنولوژی آبزیان, , , 1405, -. doi: 10.22124/jmm.2026.32516.2949
Shahul, Mohamed, Gunasekar, Tharmalingam, SANTRA, SHYAM, Baleanu, Dumitru, Yildirim, Yakup. (1405). 'A deep learning PINN-LSTM fusion framework for solving ordinary differential equations from real-life systems', فیزیولوژی و بیوتکنولوژی آبزیان, (), pp. -. doi: 10.22124/jmm.2026.32516.2949
Shahul, Mohamed, Gunasekar, Tharmalingam, SANTRA, SHYAM, Baleanu, Dumitru, Yildirim, Yakup. A deep learning PINN-LSTM fusion framework for solving ordinary differential equations from real-life systems. فیزیولوژی و بیوتکنولوژی آبزیان, 1405; (): -. doi: 10.22124/jmm.2026.32516.2949

A deep learning PINN-LSTM fusion framework for solving ordinary differential equations from real-life systems

Journal of Mathematical Modeling
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 19 تیر 1405    اصل مقاله (798.54 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22124/jmm.2026.32516.2949
نویسندگان
Mohamed Ashik Shahul1؛ Tharmalingam Gunasekar1؛ SHYAM SUNDAR SANTRA* 2؛ Dumitru Baleanu3؛ Yakup Yildirim4
1Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R\&D Institute of Science and Technology, Avadi, Chennai, India
2Khusigang, Sahapur, Bhangamora
3Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.
4Department of Computer Engineering, Biruni University, Istanbul–34010, Turkey
چکیده
This article presents a comparative study of Physics Informed Neural Networks (PINNs), Long Short Term Memory (LSTM) networks and a novel Fusion of PINNs and LSTM for solving ordinary differential equations (ODEs) arising in real world problems. After brief preliminaries on PINNs and LSTM networks, we formulate PINN and LSTM frameworks and apply each to two benchmark systems: A Religious growth population problem and Kuramoto’s two oscillator problem. We then introduce an integration strategy that fuses PINN’s physics constrained learning with LSTM’s sequential modeling to improve stability and accuracy. Extensive numerical experiments and error analyses demonstrate that the proposed fusion architecture consistently outperforms standalone PINN and LSTM models in accuracy and robustness across the two problems. The results indicate the fusion approach as a promising direction for enhanced data efficient and physically consistent solutions of complex dynamical systems.
کلیدواژه‌ها
Neural networks؛ physics-informed neural networks؛ long short-term memory networks؛ differential equations؛ Kuramoto model
آمار
تعداد مشاهده مقاله: 3
تعداد دریافت فایل اصل مقاله: 4


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب