Xiangao Jiang¹, Megan Coffee^{2, 3, *}, Anasse Bari^{4, *}, Junzhang Wang⁴, Xinyue Jiang⁵, Jianping Huang¹, Jichan Shi¹, Jianyi Dai¹, Jing Cai¹, Tianxiao Zhang⁶, Zhengxing Wu¹, Guiqing He¹, Yitong Huang⁷

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 537-551, 2020, DOI:10.32604/cmc.2020.010691

Abstract The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel… More >

Yingjun Wang¹, Zhongyuan Liao¹, Shengyu Shi^{1, *}, Zhenpei Wang^{2, *}, Leong Hien Poh³

CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 433-458, 2020, DOI:10.32604/cmes.2020.08680

Abstract Focusing on the structural optimization of auxetic materials using data-driven methods, a back-propagation neural network (BPNN) based design framework is developed for petal-shaped auxetics using isogeometric analysis. Adopting a NURBS-based parametric modelling scheme with a small number of design variables, the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method, and demonstrated in this work to give high accuracy and efficiency. Such BPNN-based fitting functions also enable an easy analytical sensitivity analysis, in contrast to the generally complex procedures of typical shape and size sensitivity More >

Fuyao Yan^{1, #}, Yu hin Chan^2,#, Abhinav Saboo³ , Jiten Shah⁴, Gregory B. Olson^{1, 3}, Wei Chen^{2, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 343-366, 2018, DOI:10.31614/cmes.2018.04452

Abstract Predicting the mechanical properties of additively manufactured parts is often a tedious process, requiring the integration of multiple stand-alone and expensive simulations. Furthermore, as properties are highly location-dependent due to repeated heating and cooling cycles, the properties prediction models must be run for multiple locations before the part-level performance can be analyzed for certification, compounding the computational expense. This work has proposed a rapid prediction framework that replaces the physics-based mechanistic models with Gaussian process metamodels, a type of machine learning model for statistical inference with limited data. The metamodels can predict the varying properties… More >

Adrien Scheuer^{1, 3, *}, Amine Ammar², Emmanuelle Abisset-Chavanne³, Elias Cueto⁴, Francisco Chinesta⁵, Roland Keunings¹, Suresh G. Advani⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 367-386, 2018, DOI:10.31614/cmes.2018.04278

Abstract Describing the orientation state of the particles is often critical in fibre suspen-sion applications. Macroscopic descriptors, the so-called second-order orientation tensor (or moment) leading the way, are often preferred due to their low computational cost. Clo-sure problems however arise when evolution equations for the moments are derived from the orientation distribution functions and the impact of the chosen closure is often unpre-dictable. In this work, our aim is to provide macroscopic simulations of orientation that are cheap, accurate and closure-free. To this end, we propose an innovative data-based approach to the upscaling of orientation kinematics… More >

Shigeru Obayashi^*, Aiko Yakeno

The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.4, pp. 71-71, 2019, DOI:10.32604/icces.2019.05229

Abstract In our laboratory, we have been conducting research focusing on a data-driven approach to fluid engineering for design. Designing aerospace machines, these days requires more advanced factors. The recent development of supercomputer leads that we must treat more complex flow phenomena that brings uncertainty, with the more advanced design considered. Such as a reduced-order model and a data assimilation method, to estimate and reduce the uncertainty in numerical simulations, are potential ways to assist the advanced design. That is by use of experimental or observational data and numerical analysis of physical equations. Recently, we are… More >