[PDE&A] Anh Khoa Doan: Scientific Machine Learning techniques for fluid mechanics

12 december 2024 16:00 t/m 17:00 - Locatie: EEMCS Hall F 36.HB.00.260 | Zet in mijn agenda

Machine learning techniques have in recent years seen an explosion in their development and application for fluid mechanics research problems. This is driven, in part, by the ability of machine learning approaches to model complex dynamics, allowing for improved predictions. Additionally, the embedding of physical information into deep learning architecture, to ensure the physicality of the predictions of the machine learning tools, is an on-going topic of research and has spurned architectures that can be used to solve inverse problems, and specifically augment the information content of a given dataset by reconstructing missing physical information.
In this talk, we will present machine learning applications along those two directions. First, we will present how a differentiable framework combined with deep learning can be used to complement an incomplete physical description of system. This will be illustrated on the case of a reacting flow, an archetypal multi-physics system that combines fluid mechanics and chemistry, the latter being the physics considered unknown. Second, we will present the use of physics-informed neural networks to augment experimental diagnostic. We will show how these can be used to reconstruct unmeasured quantities in a puffing pool fire and improve the spatial resolution of Particle Tracking Velocimetry measurements in a pulsed jet