Abstract / truncated to 115 words (read the full abstract)

The integration of physics and deep learning, known as physics-informed deep learning, has recently emerged in scientific computing and demonstrates significant potential in addressing the challenges of our time. Among various disciplines, computational fluid dynamics often faces limitations in the numerical modeling of complex fluid flow and particularly benefits from advances in physics-informed deep learning. However, understanding the applicability and limitations of this interdisciplinary field raises various questions, constituting an ongoing area of research. This thesis investigates several aspects of physics-informed deep learning for computational fluid dynamics. Encompassing topics ranging from chemical kinetics tabulation to the simulation of dynamical systems, the investigations involve several studies on how to leverage physical knowledge in the design of ... toggle 7 keywords

physics-informed neural networks – deep learning – machine learning – computational fluid dynamics – dynamical systems – scientific computing – optimization

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