Exploration and exploitation of artificial intelligence (AI) techniques have growing interest for the research community investigating in engineering and technological fields to provide improved efficiencies and augmented human abilities in daily live operations, business strategies and society evolution. A novel application of AI based backpropagating networks (BPNs) was presented for bioconvection model in transverse transportation of rheological fluid involving Lorentz force interaction and gyrotactic microorganisms. The governing nonlinear PDEs for bioconvection rheological fluidic system (BRFS) was reduced to nonlinear system of ODEs by competency of similarity adjustments. A reference data of designed BPNs was constructed for variants of BRFS representing scenarios for thermophoresis parameter, Brownian motion, Prandtl numbers, magnetic variables, squeezing and Lewis numbers by applying the Adams numerical solver. The said data were segmented arbitrary in training, testing, and validation sets to execute BPNs to calculate the approximate solutions for variants of BRFS and comparison with standard solution to validate the consistent accuracy. The worthy performance of AI based BPNs was additionally certified by learning curve on MSE based fitness, histograms and regression metrics.

人工智能（AI）技术的探索和开发对于在工程和技术领域进行研究的研究界越来越感兴趣，以在日常操作，业务策略和社会发展中提供更高的效率并增强人类的能力。提出了基于AI的反向传播网络（BPNs）在流变流体横向输送中的生物对流模型的新应用，该流变流体涉及Lorentz力相互作用和旋回微生物。通过相似性调整的能力，将用于生物对流流变流体系统（BRFS）的支配性非线性PDE简化为ODE的非线性系统。针对BRFS的变体构造了设计的BPN的参考数据，代表了热泳参数，布朗运动，普朗特数，磁变量，压缩和Lewis数，方法是应用Adams数值求解器。所述数据在训练，测试和验证集中被任意分割以执行BPN，以计算BRFS变体的近似解，并与标准解进行比较以验证一致的准确性。通过基于MSE的适应度，直方图和回归指标的学习曲线，进一步证明了基于AI的BPN的有价值表现。