Simultaneous surrogate modeling and dimension reduction using unsupervised learning. Application to parametric wing shape optimization
This paper presents a machine-learning-based approach that enables simultaneous surrogate modeling and dimension reduction and applies it to aerodynamic parametric shape optimization. Aerodynamic shape optimization is a crucial process in various industries, including aerospace, automotive, and renewable energy. It involves iteratively improving the properties of a system by evaluating an objective function and driving its minimization or maximization using an optimization algorithm. However, the evaluation of aerodynamic objective functions requires computationally