Comparative analysis of machine learning methods for active flow control

2022-02-23 · Fabio Pino, Lorenzo Schena, Jean Rabault, Miguel A. Mendez

Machine learning frameworks such as Genetic Programming (GP) and Reinforcement Learning (RL) are gaining popularity in flow control. This work presents a comparative analysis of the two, bench-marking some of their most representative algorithms against global optimization techniques such as Bayesian Optimization (BO) and Lipschitz global optimization (LIPO). First, we review the general framework of the model-free control problem, bringing together all methods as black-box optimization problems. Then, we test the control algorithms on three test cases. These are (1) the stabilization of a nonlinear dynamical system featuring frequency cross-talk, (2) the wave cancellation from a Burgers' flow and (3) the drag reduction in a cylinder wake flow. We present a comprehensive comparison to illustrate their differences in exploration versus exploitation and their balance between model capacity' in the control law definition versus required complexity'. We believe that such a comparison paves the way toward the hybridization of the various methods, and we offer some perspective on their future development in the literature on flow control problems.

📄 PDF Abstract BibTeX arXiv:2202.11664

Code (0)

등록된 구현이 없습니다.

Tasks

Bayesian OptimizationBIG-bench Machine Learningglobal-optimizationReinforcement Learning (RL)

ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion

2019-10-02 · Andreas Hinterreiter, Peter Ruch, Holger Stitz, Martin Ennemoser 외

Classifiers are among the most widely used supervised machine learning algorithms. Many classification models exist, and choosing the right one for a given task is difficult. During model selection and debugging, data sc…

Active Learning Model Selection Network Pruning

Interactive Dimensionality Reduction for Comparative Analysis

2021-06-29 · Takanori Fujiwara, Xinhai Wei, Jian Zhao, Kwan-Liu Ma

Finding the similarities and differences between groups of datasets is a fundamental analysis task. For high-dimensional data, dimensionality reduction (DR) methods are often used to find the characteristics of each grou…

Contrastive Learning Dimensionality Reduction

Active Learning using Deep Bayesian Networks for Surgical Workflow Analysis

2018-11-08 · Sebastian Bodenstedt, Dominik Rivoir, Alexander Jenke, Martin Wagner 외

For many applications in the field of computer assisted surgery, such as providing the position of a tumor, specifying the most probable tool required next by the surgeon or determining the remaining duration of surgery,…

Active Learning BIG-bench Machine Learning

Comparative Evaluation of Machine Learning Approaches for Minority-Class Financial Distress Prediction Under Class Imbalance Constraints

2026-05-13 · Karan Sehgal, Khawar Naveed Bhatti arxiv

Financial distress prediction remains a significant challenge in enterprise risk analysis due to the highly imbalanced nature of real-world financial datasets, where bankrupt or distressed firms typically constitute only…

Interpretable Machine Learning Ensemble Learning

Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single-Threaded and Multi-Threaded Modes

2017-06-07 · Yuriy Kochura, Sergii Stirenko, Anis Rojbi, Oleg Alienin 외

The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and co…

BIG-bench Machine Learning CPU GPU