kamilazdybal / local-manifold-learning Goto Github PK

I'm a postdoctoral researcher in the Computational Engineering lab at Empa 🇨🇭. In my research, I combine machine learning and data-driven modeling with fluid dynamics. I develop tools and algorithms that help understand high-dimensional datasets and model high-dimensional systems with computational efficiency.

Would you like to support my efforts in creating open-source science and education? As a supporter, you gain access to extra materials on being a researcher, making effective graphics, academic writing, life-long learning, and the like! Many thanks for your support! 🚀

► I create YouTube tutorials called Python for Academics where I teach how to automate your daily academic life. Check out 🎓 this repository for a bunch of Jupyter notebooks and Python scripts helpful in your academic adventure!

► I contribute to developing PCAfold, an open-source Python library for generating, analyzing and improving low-dimensional manifolds. Check out our SoftwareX publication and check out the tutorial videos on PCAfold.

► I develop multipy, an educational Python library intended to support your learning of multicomponent mass transfer.

► Check out the recent interview with me!

► My Ph.D. work has just been awarded the 18th ERCOFTAC da Vinci prize! My Ph.D. dissertation is freely available here: Reduced-order modeling of turbulent reacting flows using data-driven approaches.

► Our new paper Improving reduced-order models through nonlinear decoding of projection-dependent outputs is out in the journal Patterns from Cell Press!

Keep calm and $\frac{\partial \rho Y_i}{\partial t} = - \nabla \cdot (\rho Y_i \mathbf{v}) - \nabla \cdot \mathbf{j}_i + \omega_i$!