Last updated: March 07, 2026
M. Ali Yousuf
Physics-Informed Machine Learning (PIML) blends data-driven AI with established physical laws (like conservation of energy/mass, differential equations) to create more accurate, efficient, and trustworthy models, requiring less data by embedding physical constraints directly into neural networks or loss functions, leading to better generalization and the ability to discover new physics from complex scientific data.
Steve's Physics Informed Machine Learning: High Level Overview of AI and ML in Science and Engineering, https://www.youtube.com/watch?v=JoFW2uSd3Uo
The above course has 24+ videos as of December 2025. See the full list here: https://youtu.be/JoFW2uSd3Uo?si=nW_LA5JrAPGt4kXM
Zara's An Introduction to Physics-Informed Neural Networks, https://www.youtube.com/watch?v=KffYsBqrBVo.
Corresponding GitHub repo: https://github.com/zaradarz/PINN-IntroExample.
And my Google Colab notebook implementing the same, https://colab.research.google.com/drive/1NFcrDrxyVnEV5enzLaxFmflDGFiF3JUU?usp=sharing
ChatGPT generated Google Colab notebook: Physics-Informed Machine Learning (PIML) example using a Physics-Informed Neural Network (PINN), https://colab.research.google.com/drive/1EqckMKdLXmMdO7uX2lvQs7nIJlZ8DLN4?usp=sharing
Jordan's How Do Physics-Informed Neural Networks Work? https://www.youtube.com/watch?v=pbt3Ztkwwz8
Juan's PINNS: Learning Physics Informed Machine Learning (3 parts) https://www.youtube.com/playlist?list=PLSbVktDWdy9T2hBo5s8Rq42nMpGRekJ75
CAMLab, ETH Zürich's Physics-Informed Neural Networks – Introduction, https://www.youtube.com/watch?v=D-F7BYRhAkQ&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r&t=1s
Physics-Informed Machine Learning — PIML, https://medium.com/@joris.c/physics-informed-machine-learning-piml-debe8f856c10
Physics-informed Neural Networks: a simple tutorial with PyTorch, https://medium.com/@theo.wolf/physics-informed-neural-networks-a-simple-tutorial-with-pytorch-f28a890b874a
Introduction to Physics-informed Neural Networks - A hands-on tutorial with PyTorch, https://medium.com/data-science/solving-differential-equations-with-neural-networks-afdcf7b8bcc4
Physics-informed neural networks, https://en.wikipedia.org/wiki/Physics-informed_neural_networks
2025 - Doumeche - Physics-informed machine learning: A mathematical framework with applications to time series forecasting, https://arxiv.org/abs/2507.08906#:~:text=Physics%2Dinformed%20machine%20learning%20(PIML,the%20regression%20function%20must%20satisfy.
2024 - Toscano - From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning, https://arxiv.org/abs/2410.13228
2024 - Baty - A hands-on introduction to Physics-Informed Neural Networks for solving partial differential equations with benchmark tests taken from astrophysics and plasma physics, https://arxiv.org/html/2403.00599v1
2023 - Xu - Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges, https://www.sciencedirect.com/science/article/abs/pii/S0951832022005154?via%3Dihub
2022 - Hao - Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications, https://arxiv.org/abs/2211.08064
2020 - Raissi - Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations, https://www.science.org/doi/10.1126/science.aaw4741
2019 - Raissi - Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, https://www.sciencedirect.com/science/article/abs/pii/S0021999118307125
Physics-Informed Neural Network (PINN), https://symtorch.readthedocs.io/en/latest/demos/pinns_demo.html