Building Models from Data and Scientific Principles
With the MATLAB and Simulink product families you can model virtually any type of system, including:
You can choose from several modeling environments, enabling you to describe your system programmatically, symbolically, or with block diagrams and state machines. Creating data-driven or physics-based models enable many different benefits such as extracting insight from data, informing design processes through Model-Based Design, enabling Virtual Commissioning, or creating operational digital twins.
Develop Models from Data
When you have physical insight, you can create models from first principles using analytic or symbolic approaches. Data-driven modeling techniques are especially useful when you do not have sufficient information about your system. In this case, you can ensure model accuracy by choosing a modeling technique that is right for your experimental or historical data. Use statistics and curve fitting tools to explore relationships among your data. You can use linear and nonlinear regression models, classification, clustering, and surface fitting tools. Dynamic models that allow you to express the effect of a system’s past experiences on its current and future behavior can be modeled using neural networks and system identification techniques. Data-driven techniques can also be used to tune the coefficients of your first-principles model in order to fit experimental data using grey-box modeling and response optimization techniques.
Develop Models Based on Mathematical, Engineering, and Scientific Principles
You can choose from multiple approaches for creating mathematical models based on first principles. For example, you can:
Develop Models for Domain-Specific Applications
MathWorks application-specific products let you develop mathematical models for applications in the following areas:
- Computational finance portfolio optimization, risk estimation, and economic forecasting
- Physical modeling mechanical, electrical, hydraulic, and drive-line systems
- Powertrain modeling and calibration
- Computational biology gene expression analysis, sequence analysis, and pathway modeling
- Aerospace systems environmental and aerodynamic modeling
- Control systems plant modeling, controller design and verification, closed loop system simulation