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Machine Learning in Physics
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This recipe introduces Machine Learning in Physics. It describes the motive for applying ML in physics, and discusses current progress and challenges in the field with detail.
Potential Use Cases
Incorporating physics into ML models, Using ML models to learn physics
Who is This For ?
Physics researchers, machine learning engineers incorporating physics
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to see details.
1. Introduction to Machine Learning for Physics
How do you teach physics to machine learning models?
2. ML in Physics and the Physics of Learning
What are the challenges of applying ML to Physics? How can ML Models leverage physical laws? What kind of models are used for each physics problem?
3. Application of ML in Fluid Dynamics
How can we use ML to solve a physics problem: turbulence modelling?
4. Algorithm for backpropagation
Pseudocode for Back Propagation
5. Intuitive understanding of Backward Propagation
What is Forward Propagation?
What is Backward Propagation?
6. Convolutional Neural Network
What is a convolutional neural network?
7. Encoder-Decoder network explained
What is encoder- decoder network ?