Scientific Machine Learning (SciML) is an approach that integrates machine learning with physical and scientific principles to model, simulate, and predict complex phenomena. In transport phenomena (such as fluid dynamics, heat transfer, and mass transport), SciML enables the development of models that combine experimental or simulated data with known physical laws, enhancing the accuracy and reliability of predictions.
Data-Driven Modeling#
Data-driven modeling is central to SciML, allowing complex patterns present in large datasets to be identified without explicitly deriving complete differential equations. This approach is particularly useful in transport phenomena, where nonlinear interactions and multiscale effects make analytical modeling difficult or infeasible.
Key Applications#
SciML in transport offers several strategic applications, including:
- Creation of Surrogate Models
Replace computationally intensive simulations with fast models, enabling real-time optimization and control.
- Uncertainty Quantification
Estimates the reliability of predictions and the impact of variability in system parameters, which is essential for safe and robust decision-making.
- Reduced-Order Models
Capture the essential dynamics of complex systems with fewer variables, reducing computational cost without significant loss of accuracy.
- Governing Equation Discovery
Identifies underlying physical laws or empirical correlations directly from experimental or simulation data, using techniques such as Physics-Informed Neural Networks (PINNs) and Sparse Identification of Nonlinear Dynamics (SINDy).
- Optimization and Control of Transport Systems
Applications in aerodynamics, thermal systems, and industrial transport processes, where accurate and fast modeling enables improved performance and efficiency.
Benefits of SciML#
- Reduces the number of experiments or simulations required;
- Increases interpretability and consistency with physical laws;
- Capable of handling multiscale and nonlinear systems;
- Facilitates integration of experimental and simulation data;
- Enables the development of fast and scalable solutions for engineering, applied sciences, and industrial research.
Concluding Remarks#
SciML provides a powerful paradigm for modeling and analyzing transport phenomena, combining the strengths of machine learning and physical knowledge to create more accurate, faster, and reliable models, paving the way for advanced applications in engineering, energy, biomedicine, and environmental sciences.