machine learning in engineering

AI-assisted product development describes the use of AI throughout the development process from concept to validation. It can structure requirements, accelerate simulations, evaluate variants, analyse test data and prepare engineering decisions. In CAE processes, it is especially useful when large datasets from DoE, CFD, test benches or operation are available. The greatest value arises when AI is combined with clear engineering targets, domain knowledge and traceable validation.

AI-assisted simulation uses artificial intelligence to make simulation processes faster, more robust or easier to evaluate. Examples include automated meshing, parameter selection, surrogate models, result prediction, error detection and fast approximation of expensive CFD or FEA calculations. AI is especially valuable when many operating points, geometry variants or optimization loops must be investigated. However, AI must be checked against physical references because a fast model is not automatically a correct model.

Artificial intelligence in engineering includes methods that analyse technical data, identify patterns, automate models or support decisions. This includes machine learning, optimization, image and signal analysis, language models, knowledge systems and automated postprocessing. In product development, AI can shorten development time, make simulation data more useful and evaluate variants more systematically. AI does not replace engineering expertise, but extends the toolchain when data quality, model limitations and validation are well controlled.

A classification model assigns data points to fixed classes or categories. In engineering, it can distinguish between pass and fail, stable and unstable, healthy and faulty, or surge and no surge. Classification is useful when the goal is not an exact numerical value but a technical decision. Clear classes, balanced training data and evaluation of misclassification are important.

Data-driven modelling builds a model mainly from available data rather than from explicitly formulated physical equations. The data can come from simulations, measurements, test benches, vehicle operation or design of experiments. Such models are often fast and can approximate complex relationships if the application range is well covered by training data. Caution is required outside the data range because data-driven models can produce physically wrong predictions there.

Data preprocessing includes all steps that make raw data usable for machine learning. This includes cleaning, scaling, normalization, outlier checks, unit alignment, handling missing values and combining different data sources. In engineering, preprocessing is especially important because simulation and measurement data often have different formats, noise levels or boundary conditions. Poor preprocessing often leads to poor models, even when the algorithm itself is powerful.

A decision tree is a model that splits data through successive if-then decisions. It is easy to understand and can be used for both regression and classification. In engineering, it is useful for making simple decision rules or influencing variables visible. Single decision trees tend to overfit, however, and are often less robust than methods such as random forest or gradient boosting.

Deep learning refers to machine learning with deep neural networks. It is especially strong for large datasets, complex patterns and high-dimensional input data. In engineering, deep learning can be used for image processing, anomaly detection, field prediction, surrogate models and automated evaluation. Its technical value strongly depends on having enough representative data and clear validation metrics.

A deep neural network has multiple hidden layers and can therefore approximate very complex functions. It is especially suitable for large datasets, high-dimensional inputs, images, field data or complex surrogate models. In CAE contexts, deep networks can approximate flow fields, temperature fields or result metrics, for example. However, the effort for training, data preparation, regularization and interpretability is higher than for simple models.

A Gaussian process is a probabilistic regression model that provides predictions together with uncertainty. This is especially useful in engineering because expensive simulations can be added selectively where uncertainty is high. Gaussian processes are often used for surrogate models, Bayesian optimization and small to medium-sized datasets. For very large datasets or very high dimensionality, however, they can become computationally expensive.

Gradient boosting is a method in which many weak models are gradually combined into a strong model. Each new tree corrects errors made by the previous models. In engineering, gradient boosting can provide very accurate predictions for tabular simulation and measurement data. It is powerful, but requires careful hyperparameter selection and validation to avoid overfitting.

Hybrid modelling combines physics-based models with data-driven methods. A typical example is a CFD or 1D model that is accelerated, corrected ols are especially interesting for surrogate models, digital twins, control, optimization and simulations with many variants.r supplemented by a machine learning model. This can combine physical plausibility with high computational speed. Hybrid mode

Kriging is an interpolation and regression method often used in engineering surrogate models. It is closely related to Gaussian processes and can generate smooth response surfaces from limited sampling points. In engineering, Kriging is commonly used for DoE, optimization, sensitivity analysis and surrogate models of expensive simulations. Its advantage is good accuracy with few sampling points, while its disadvantages are modelling effort and sensitivity to data quality.

Linear regression describes a target quantity as a linear combination of input variables. It is simple, transparent and easy to interpret. In engineering, it is suitable for first trend analyses, simple correlations and robust baseline models. Its limitation appears for strongly nonlinear relationships, interactions or complex physical effects.

A metamodel is a model built from the results of other models or experiments. It describes the relationship between parameters and results at a higher abstraction level. In CAE processes, a metamodel is often generated from a DoE with many CFD or FEA runs. It is used to analyse relationships faster, identify sensitivities and perform optimization more efficiently.

Model order reduction describes methods that reduce complex simulation models to a smaller and faster model form. The goal is to preserve the most important dynamic or physical properties. It is used when detailed models are too slow for optimization, control, real-time applications or large variant studies. The quality of model order reduction must always be checked against the original model or measurement data.

A neural network is a machine learning model made of connected computational units called neurons. It can learn complex nonlinear relationships between inputs and outputs. In engineering, it is used for surrogate models, map replacement, image evaluation, sensor signals, condition monitoring and optimization. Neural networks can be very powerful, but require sufficient data, good scaling and careful validation.

Nonlinear regression describes relationships that cannot be captured by a simple linear function. In engineering, this is often the normal case, for example for flow resistance, heat transfer, material behaviour or efficiency maps. Nonlinear models can be much more accurate, but require more data and more careful validation. It is important that the chosen model form remains technically plausible and does not merely fit training data.

Physics-based modelling describes models based on physical equations, conservation laws and known material or fluid laws. Examples include CFD, FEA, 1D engine simulation and thermal networks. The advantage is better extrapolation capability when the relevant physical mechanisms are represented correctly. The disadvantage is often higher modelling effort, longer computation time and necessary assumptions about boundary conditions, material data and model parameters.

A physics-informed neural network, or PINN, combines neural networks with physical equations or constraints. The model learns not only from data, but is additionally guided by conservation equations, boundary conditions or known physical laws. This can produce more physically plausible solutions than a purely data-driven network when data is limited. PINNs are especially interesting for fluid flow, heat transfer, inverse problems and model reduction, but they are not automatically faster or simpler than classical simulation.

PINN stands for physics-informed neural network and describes a neural network that includes physical equations or constraints in addition to training data. The model is guided by conservation equations, boundary conditions, initial conditions or known material laws. This can make a PINN more physically plausible than a purely data-driven model when data is limited. In engineering applications, PINNs are especially interesting for fluid simulation, heat transfer, inverse problems and model reduction.

Polynomial regression extends linear regression with powers and interactions of input variables. This allows curved relationships to be described more easily. It is well suited for manageable response surfaces with moderate nonlinearity. With high polynomial order or many parameters, however, it can become unstable and strongly overfit.

Random forest is a machine learning method made of many decision trees. It can robustly represent nonlinear relationships and interactions. In engineering, it is suitable for regression, classification, feature importance and fast surrogate models. Its disadvantage is that random forests do not extrapolate smoothly and are often only partly reliable outside the training range.

A reduced order model, or ROM, is a simplified model with significantly reduced mathematical complexity. It aims to reproduce the essential behaviour of a detailed model with much shorter computation time. In engineering, ROMs are used for fluid flow, structures, thermal systems, control and digital twins. The key is that the reduction preserves the effects relevant to the engineering question and does not merely save computation time.

A regression model predicts continuous target quantities. In engineering, these can include drag, pressure loss, temperature, efficiency, stress or lifetime. Regression models learn from data how input variables relate to a numerical target quantity. They are especially suitable for surrogates, map replacement, trend analysis and fast predictions in optimization loops.

Response Surfaces sind gut verständlich, können aber bei stark nichtlinearen oder hochdimensionalen Zusammenhängen ungenau werden. | A response surface is a mathematical surface that describes the relationship between input parameters and a target quantity. It is often built from design of experiments or simulation data. In product development, it helps identify trends, optima and sensitivities quickly. Response surfaces are easy to understand, but can become inaccurate for strongly nonlinear or high-dimensional relationships.

A response surface model is a surrogate model that describes an engineering target quantity as a function of multiple input parameters. It is often used for DoE evaluation, optimization and variant comparison. Examples include drag over vehicle ride height and yaw angle or pressure loss over volume flow and geometry parameters. Model quality strongly depends on the selected sampling points and the chosen approximation function.

A support vector machine is a method for classification and regression. It finds a decision boundary or regression function that describes data points with good separation or error control. With kernel functions, nonlinear relationships can also be captured. In engineering, it is useful for small to medium-sized datasets, but can become computationally expensive for large datasets.

A surrogate model is a fast replacement model for a slower, more expensive or more complex reference model. In engineering, it often replaces CFD, FEA, 1D simulation or test bench data in variant studies and optimization. The surrogate learns the relationship between input variables and target quantities from training data. Its validity range must be clearly defined because a surrogate model can become unreliable outside the trained data domain.

Test data is used to evaluate model quality on previously unseen data. It should not be used for training or parameter selection so that the evaluation remains as independent as possible. In engineering, test data shows whether a surrogate model or regression model is reliable for new geometries, operating points or boundary conditions. A clear separation between training data, validation data and test data prevents overly optimistic results.

A test dataset is the separate dataset used to finally evaluate a completed model. It should contain representative cases that the model has not seen during training or validation. In CAE and engineering applications, the test dataset can consist of additional CFD runs, measurements or deliberately withheld DoE points. It provides the most important evidence of whether a model works reliably outside the training data.

Training data is the data used by a machine learning model to learn relationships. In engineering, it often comes from CFD simulations, FEA runs, test bench data, measurement series, DoE studies or operational data. The quality of the training data largely determines whether the model can make robust predictions later. Representative operating points, clean target values, consistent units and good coverage of the parameter space are important.

A training dataset is the structured collection of training data for a machine learning model. It contains input parameters, target variables and often additional metadata such as geometry version, simulation setup, boundary conditions or measurement uncertainty. For engineering models, not only the number of data points matters, but also their distribution in the parameter space. A good training dataset is traceable, cleaned and physically plausible.

Validation data is used during model development to select model variants, hyperparameters or training stopping points. It is therefore used for model selection, not for final independent evaluation. In engineering projects, validation data helps detect overfitting early and assess model quality in the relevant parameter range. The final statement about transferability should still be based on separate test data.

XGBoost is a widely used and efficient implementation of gradient boosting. It is often used for structured tabular data and frequently achieves high accuracy with relatively short training time. In engineering, it is suitable for surrogate models, fault classification, quality prediction and fast variant evaluation. As with all ML models, data quality, feature selection and validation are more important than the algorithm name itself.