Physics-Informed Machine Learning
Big data and ever-increasing computational power has meant that, in many areas of business and science, AI has become a powerful tool in inference and classification. However, for areas like the physical sciences, where the data is based on variables that might be extremely difficult or impossible to measure, data is hard to produce. This is where the concept of physics-informed machine learning comes into play.
Using this concept, my research has enabled the design of novel biomedical devices, improved the detection of mild traumatic brain injury, enabled better oil-well performance predictions, and continues to push the bounds of what is currently possible using simulation and AI together. This new area of research is pregnant with possibilities and combines many new areas together to solve some of the hardest problems.
My current focus is on the three pillars of physics-informed machine learning:
- Pre-Training: Using simulated results to navigate deep learning to a point where the limited amount of real-world data can push the model to its optimal state.
- Physics-based Loss Functions: Adding terms like conservation of energy, momentum, and other important laws into the optimization process to improve model generalizability and ensure realistic predictions.
- Synthetic Data for Inverse Modeling: Some models that predict how a system will evolve from one point to another cannot be inverted to go backwards. Synthetic datasets from these numerical models can be used to train a deep learning model to let us invert these problems and discover the initial or boundary conditions.