A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems

In this work, we present an extension of genetic algorithm (GA) which exploits the supervised learning technique called active subspaces (AS) to evolve the individuals on a lower-dimensional space. In many cases, GA requires in fact more function evaluations than other optimization methods to converge to the global optimum. Thus, complex and high-dimensional functions can end up extremely demanding (from the computational point of view) to be optimized with the standard algorithm. To address this issue, we propose to linearly map the input parameter space of the original function onto its AS before the evolution, performing the mutation and mate processes in a lower-dimensional space. In this contribution, we describe the novel method called ASGA, presenting differences and similarities with the standard GA method. We test the proposed method over n-dimensional benchmark functions-Rosenbrock, Ackley, Bohachevsky, Rastrigin, Schaffer N. 7, and Zakharov-and finally we apply it to an aeronautical shape optimization problem

Studi Kelayakan LKS Praktikum Berbasis Pendekatan Saintifik Serta Dampaknya pada Hasil Belajar Materi Sifat Larutan Penyangga

Penelitian ini bertujuan untuk mengetahui kelayakan produk LKS praktikum berbasis pendekatan saintifik dan mengetahui dampaknya pada hasil belajar khususnya  materi sifat larutan penyangga dengan melihat seberapa banyak siswa yang mencapai  atau melampaui KKM. Jenis penelitian ini adalah penelitian dan pengembangan yang dilakukan di kelas XI IPA 5 SMAN 1 Tondano. Berdasarkan hasil rekapitulasi, LKS praktikum berbasis pendekatan saintifik dinyatakan valid dengan nilai sebesar 83,8%. Dilihat dari respon siswa pada uji coba kelompok kecil dan kelompok besar LKS dinyatakan praktis dengan nilai berturut-turut sebesar 88,17% dan 92,98%. Dampak penggunaan LKS terhadap hasil belajar siswa adalah sebagian besar siswa pada uji coba kelompok kecil dan siswa pada uji coba kelompok besar telah melampaui KKM yang sudah ditetapkan dengan nilai ketuntasan yang diperoleh berturut-turut sebesar 66,67% dan 89,47%. Pengujian hipotesis untuk hasil belajar menggunakan uji t pihak kiri diperoleh nilai thitung(8,517) > ttabel(1,734) yang berarti terima H0 atau tolak H1. Berdasarkan hasil yang diperoleh dapat disimpulkan bahwa LKS praktikum berbasis pendekatan saintifik yang dikembangkan layak digunakan dalam proses pembelajaran

Enhancing CFD predictions in shape design problems by model and parameter space reduction

In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD

Enhancing CFD predictions in shape design problems by model and parameter space reduction

In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD

Reduced order isogeometric analysis approach for PDEs in parametrized domains

In this contribution, we coupled the isogeometric analysis to a reduced order modelling technique in order to provide a computationally efficient solution in parametric domains. In details, we adopt the free-form deformation method to obtain the parametric formulation of the domain and proper orthogonal decomposition with interpolation for the computational reduction of the model. This technique provides a real-time solution for any parameter by combining several solutions, in this case computed using isogeometric analysis on different geometrical configurations of the domain, properly mapped into a reference configuration. We underline that this reduced order model requires only the full-order solutions, making this approach non-intrusive. We present in this work the results of the application of this methodology to a heat conduction problem inside a deformable collector pipe