Physics-based Deep Learning

Physics-based Deep Learning

Physics-based Deep Learning refers to the integration of deep learning techniques with the principles and laws of physics to improve the performance and understanding of complex systems. It involves incorporating knowledge from physics, such as conservation laws, symmetries, and physical constraints, into the training and modeling process of deep neural networks.

By leveraging the inherent structure and patterns found in physical systems, physics-based deep learning enables the development of more accurate and efficient models that can capture the underlying dynamics and behaviors of these systems. This approach allows researchers to simulate and predict the behavior of physical phenomena with greater precision and fidelity.

• Physics-based Deep Learning
  • thunil/Physics-Based-Deep-Learning
  • Physics-based Deep Learning-ebook
• SciML - WUR Scientific Machine Learning Network
• GitHub - benmoseley/harmonic-oscillator-pinn