Almas Temirbekov¹, Zhadra Zhaksylykova^2,*, Yerzhan Malgazhdarov³, Syrym Kasenov¹

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2035-2055, 2022, DOI:10.32604/cmc.2022.027830

Abstract To apply the fictitious domain method and conduct numerical experiments, a boundary value problem for an ordinary differential equation is considered. The results of numerical calculations for different values of the iterative parameter τ and the small parameter ε are presented. A study of the auxiliary problem of the fictitious domain method for Navier-Stokes equations with continuation into a fictitious subdomain by higher coefficients with a small parameter is carried out. A generalized solution of the auxiliary problem of the fictitious domain method with continuation by higher coefficients with a small parameter is determined. After all the above mathematical studies,… More >

Ali Raza^1,2,*, Dumitru Baleanu^3,4, Muhammad Rafiq⁵, Syed Zaheer Abbas⁶, Abubakar Siddique⁶, Umer Javed⁸, Mehvish Naz⁷, Arooj Fatima⁶, Tayyba Munawar⁶, Hira Batool⁶, Zaighum Nazir⁶

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3621-3634, 2022, DOI:10.32604/cmc.2022.023286

Abstract Cancer is a common term for many diseases that can affect any part of the body. In 2020, ten million people will die due to cancer. A worldwide leading cause of death is cancer by the World Health Organization (WHO) report. Interaction of cancer cells, viral therapy, and immune response are identified in this model. Mathematical and computational modeling is an effective tool to predict the dynamics of cancer virotherapy. The cell population is categorized into three parts like uninfected cells (x), infected cells (y), virus-free cells (v), and immune cells (z). The modeling of cancer-like diseases is based on… More >

Shaofei Ren^1,2, Guorong Chen² , Tiange Li² , Qijun Chen², Shaofan Li^{2, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 287-307, 2018, DOI:10.31614/cmes.2018.04697

Abstract In this work, we have developed a novel machine (deep) learning computational framework to determine and identify damage loading parameters (conditions) for structures and materials based on the permanent or residual plastic deformation distribution or damage state of the structure. We have shown that the developed machine learning algorithm can accurately and (practically) uniquely identify both prior static as well as impact loading conditions in an inverse manner, based on the residual plastic strain and plastic deformation as forensic signatures. The paper presents the detailed machine learning algorithm, data acquisition and learning processes, and validation/verification examples. This development may have… More >