Physics-informed machine learning (PIML) is an emerging field that incorporates physical laws and principles into machine learning models. This approach leverages the known underlying physics of a system to guide the learning process, making it more interpretable and accurate.

Here’s a general overview:

1. Integration of Physical Laws: PIML incorporates governing equations, conservation laws, and boundary conditions that are well understood in physics to constrain the model. This can make the learning process more efficient by narrowing the hypothesis space.
2. Data Efficiency: By incorporating prior knowledge about the system, PIML models often require fewer data to achieve accurate predictions. This can be particularly useful when collecting data is expensive or challenging.
3. Model Interpretability: Traditional machine learning models can act like “black boxes,” where it’s unclear how they arrive at a particular prediction. PIML models, by contrast, can provide more insight into their decision-making processes by aligning with known physical principles.
4. Hybrid Models: PIML often involves the creation of hybrid models that combine physics-based modeling with data-driven techniques. This allows for the best of both worlds, leveraging the predictive power of machine learning with the structural insights from physics.
5. Applications: PIML has a wide range of applications, including fluid dynamics, material science, climate modeling, and more. It’s a powerful tool for researchers and engineers who need to model complex systems with limited data.