What is supervised machine learning and how does it apply to engineering design?

1 Basic concepts

Supervised machine learning requires a set of input variables (also called features or predictors) and a corresponding output variable (also called target or response). The input variables can be numerical, categorical, or textual, and the output variable can be discrete (for classification) or continuous (for regression). The goal of supervised machine learning is to find a function that maps the input variables to the output variable, based on a training dataset that contains examples of the desired input-output pairs. The function is also called a model, and it can have different forms, such as linear, nonlinear, or probabilistic.

2 Types of algorithms and models

Supervised machine learning algorithms and models come with their own strengths, weaknesses, and assumptions. Linear regression is a simple and widely used model that assumes a linear relationship between the input and output variables, and can be used for regression tasks such as predicting the cost or performance of a design. Logistic regression is a variation of linear regression that can handle binary output variables, such as yes/no or pass/fail, and can be used for classification problems such as predicting the feasibility or reliability of a design. Decision tree is a graphical model that splits the input space into regions based on a series of rules or questions, and can be applied to both classification and regression problems. Neural networks are complex and powerful models that mimic the structure and function of the human brain, consisting of layers of interconnected nodes that process and transmit information. They can be used for both classification and regression tasks, such as recognizing patterns or generating designs.

4 Examples of applications

Supervised machine learning is a powerful tool with a variety of applications in engineering design. For instance, neural networks can be used to predict the properties and performance of materials based on their composition, structure, or processing. Similarly, decision trees can be used to detect and identify faults or anomalies in engineering systems based on their signals, symptoms, or features. Additionally, linear regression can be used to optimize the design of engineering systems based on their objectives, constraints, or preferences. In this way, supervised machine learning has great potential to improve the design of engineering systems.