Physics-Based Modeling vs. Data-Driven Modeling

Engineering teams increasingly have access to both physics-based simulation and data-driven modeling. These approaches are sometimes presented as competitors, but they answer different kinds of questions and often become more powerful when used together.

Physics-based modeling

Physics-based models begin with an understanding of the mechanisms governing a system. Conservation of mass, momentum, and energy; thermodynamic relationships; transport processes; reaction kinetics; and other physical constraints provide the structure of the model.

This approach is especially valuable when engineers need to understand why a system behaves as it does, explore conditions outside available experimental data, or evaluate design changes before prototypes exist.

Data-driven modeling

Data-driven models infer relationships from observations. They can identify patterns in complex datasets and provide efficient predictions when sufficient representative data are available.

Their usefulness depends strongly on the quantity, quality, and relevance of the data used to develop them. Predictions outside the range represented by those data require particular care.

The engineering question should drive the choice

The appropriate method depends on the decision being supported. A mechanistic model may be preferable when physical interpretation, extrapolation, or design understanding is important. A data-driven model may be preferable when rich datasets exist and rapid prediction is the primary objective.

Hybrid physics-data approaches

Many engineering problems benefit from combining the two. Physics can constrain the solution space while data can estimate uncertain parameters, correct model deficiencies, accelerate computationally expensive calculations, or reveal relationships that deserve further mechanistic investigation.

From model sophistication to useful decisions

A more complicated model is not automatically a better model. The objective is to reduce uncertainty around a technical decision. Model fidelity, computational cost, available data, uncertainty, and interpretability should therefore be considered together.