[libROM tutorial] A complete derivation of dynamic mode decomposition (DMD) |

Описание: 🚀 Unlocking Insights with Dynamic Mode Decomposition (DMD): A Comprehensive Tutorial

Dive into the world of dynamic mode decomposition with our step-by-step tutorial! 🌐 Dynamic Mode Decomposition is a powerful data-driven technique with applications ranging from control systems to surrogate modeling. In this video, we break down the intricate details, providing you with a complete derivation of DMD.

🎓 Who Should Watch:

Engineers and scientists interested in data-driven methods
Anyone seeking a deep understanding of Dynamic Mode Decomposition
Anyone looking for an open source library for Dynamic Mode Decomposition

🔗 Command line options to generate results shown in the YouTube tutorial:
mpirun -np 8 ./heat_conduction -s 1 -a 0.0 -k 1.0 -rs 2 -rp 2
mpirun -np 8 ./heat_conduction -s 1 -a 0.0 -k 1.0 -rs 2 -rp 3
mpirun -np 8 ./heat_conduction -s 1 -a 0.0 -k 1.0 -rs 2 -rp 4
mpirun -np 8 ./heat_conduction -s 1 -a 0.0 -k 1.0 -rs 2 -rp 5

📈 Stay Tuned for next tutorial:
Parametric dynamic mode decomposition

🔗 Connect with Us:
libROM webpage: www.librom.net
libROM GitHub repo: https://github.com/LLNL/libROM
pylibROM GitHub repo: https://github.com/LLNL/pylibROM

A list of libROM tutorials:

1. Poisson equation & its finite element discretization (    • [libROM tutorial] Poisson equation & its f...   )
2. Projection-based reduced order model for Poisson equation (    • [libROM tutorial] Projection-based reduced...   )
3. Physics-informed sampling method for reduced order models (    • [libROM tutorial] Physics-informed samplin...   )
4. Local reduced order models and interpolation-based parameterization (    • [libROM tutorial] Local reduced order mode...   )
5. Projection-based reduced order model for nonlinear system (    • [libROM tutorial] Projection-based reduced...   )
6. A complete derivation of dynamic mode decomposition (    • [libROM tutorial] A complete derivation of...   )

👉 Don't miss out on mastering Dynamic Mode Decomposition! Watch now and revolutionize your approach to data-driven analysis. 🚀

If you have any questions, feel free to leave your comments below.