Artificial Intelligence and Fluid Mechanics
Our research at the intersection of artificial intelligence and fluid mechanics aims to transform computational approaches to complex fluid systems. By developing advanced machine learning techniques, we are creating innovative methods to analyze and predict fluid behaviors across critical technological domains.
We focus on two primary research directions. First, we use AI techniques like autoencoders and symbolic regression to simplify complex chemical reaction mechanisms, reducing computational complexity. Secondly, we are developing neural operator methods for more efficient computational fluid dynamics (CFD) simulations, with particular applications in sustainable technologies such as wind turbine aerodynamics and advanced thermal cycles.
Our collaborative approach involves partners from research institutions and industry, leveraging sophisticated simulation tools to model everything from ice accretion on wind turbine blades to high-performance turbomachinery. By integrating physics-based principles with cutting-edge computational techniques, we aim to provide more rapid, nuanced predictions of fluid system behaviors, ultimately contributing to more efficient and sustainable technological solutions.
Contact
Magnus Genrup
magnus [dot] genrup [at] energy [dot] lth [dot] se (magnus[dot]genrup[at]energy[dot]lth[dot]se)
+46 46 222 92 77