AI/ML+Physics: Preview of Upcoming Modules and Bootcamps

The Parsimonious modeling and model discovery focus on dynamical systems that change over time. Systems such as brains, financial markets, climate epidemiology, and other complex systems evolve according to differential equations. These equations can be high-dimensional, stochastic, or nonlinear, capturing the complexity of the world around us. The module on Parsimonious modeling of dynamical systems is centered on learning these equations solely from data using machine learning algorithms.

This area is exciting as it allows us to learn fundamental laws like Kepler's Law or F equals ma from data, and even make corrections like Einstein's correction based on discrepancies in observations. These principles can be applied to new problems, such as modeling plasma systems, granular material, or neuroscience. The ability to build models for the brain based on measurement data is particularly intriguing.

Another key concept discussed is the use of pins in neural networks. In addition to standard loss functions, pins are used when training neural networks to predict or reconstruct physical fields like fluid velocity fields. These fields vary in space and time, adding another layer of complexity to the modeling process.