Lesson Summary

Scientific machine learning is an emerging field that combines traditional scientific computing with modern machine learning techniques. It aims to leverage the domain knowledge and advancements in machine learning to solve complex problems in various scientific fields. By integrating physics with machine learning, scientists can train models using small or noisy datasets, which is a common challenge in supervised learning. This approach allows for the utilization of both physics-based knowledge and the capabilities of machine learning algorithms. One key technology in scientific machine learning is the use of Physics Informed Neural Networks (PINNs). PINNs combine the principles of physics with neural networks, which are widely used in machine learning. Neural networks can be trained to learn complex patterns and relationships in data, while PINNs incorporate known physical laws and constraints into the training process. This fusion allows for the development of models that can accurately represent the underlying physics while also capturing intricate patterns in the data. Scientific machine learning provides a middle ground between purely data-driven models and solely physics-based approaches. While data-driven models without physics tend to lack extrapolation capabilities and fail when tested on new data, solely physics-based models may struggle with limited or noisy data. By merging physics and machine learning, researchers aim to overcome these limitations and tackle challenging problems in scientific computing and geophysics. In summary, scientific machine learning integrates traditional scientific computing with modern machine learning techniques to solve complex problems. By combining physics-based knowledge with machine learning algorithms, scientists can train models using small or noisy datasets. The use of physics informed neural networks allows for the development of models that capture both the underlying physics and complex data patterns. This approach provides a middle ground between purely data-driven models and solely physics-based approaches, unlocking new possibilities in scientific research.