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  1. 2361

    On nonlinear coupled differential system for heat transfer in magnetized enclosure with T-shaped baffle by using machine learning by Khalil Ur Rehman, Wasfi Shatanawi, Lok Yian Yian

    Published 2025-03-01
    “…It is well consensus among researchers that the constructing mathematical model for heat transfer problems results set of coupled nonlinear partial differential equations (PDEs) and the solution in this regard gets a challenging task. …”
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  3. 2363

    Carreau fluid flow analysis with inclined magnetic field and melting heat transfer by Rasheed Khan, Salman Zeb, Zakir Ullah, Muhammad Yousaf, Inna Samuilik

    Published 2025-03-01
    “…The problem is formulated as a system of nonlinear partial differential equations and using similarity transformations these are converted to non-linear ordinary differential equations. …”
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  4. 2364
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    Heat transfer with magnetic force and slip velocity on non-Newtonian fluid flow through a porous medium by Muhammad Ramzan, Muhammad Shahryar, Shajar Abbas, Muhammad Amir, Shaxnoza Ravshanbekovna Saydaxmetova, Rashid Jan, Afnan Al Agha, Hakim AL Garalleh

    Published 2025-03-01
    “…Model validation is achieved by comparing results obtained through algorithmic solutions, confirming the robustness of the fractional approach by taking the values of fractional parameter γ in the range 0.2≤γ≤1, whereas the values of slip parameter λ lies in the range 0≤λ≤0.8. …”
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  11. 2371

    Normalizing flow-assisted nested sampling on Type-II Seesaw model by Rajneil Baruah, Subhadeep Mondal, Sunando Kumar Patra, Satyajit Roy

    Published 2025-07-01
    “…Abstract We propose a novel technique for sampling particle physics model parameter space. The main sampling method applied is nested sampling (NS), which is boosted by the application of multiple machine learning (ML) networks, e.g., self-normalizing network (SNN) and Normalizing Flow (specifically RealNVP). …”
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    Probing light scalars and vector-like quarks at the high-luminosity LHC by U. S. Qureshi, A. Gurrola, A. Flórez, C. Rodriguez

    Published 2025-04-01
    “…We perform a phenomenological analysis considering $$\chi _\textrm{u}$$ χ u final states to b-quarks, muons, and neutrinos, and $$\phi '$$ ϕ ′ decays to $$\mu ^+\mu ^-$$ μ + μ - . A machine learning algorithm is used to maximize the signal sensitivity, considering an integrated luminosity of 3000 $$\text {fb}^{-1}$$ fb - 1 . …”
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