Fluid dynamics simulations play a crucial role in analyzing the behavior of fluids in a wide range of scientific and engineering applications, including aerospace and automotive design. However, Computational Fluid Dynamicss (CFDs) demands substantial computational resources for storage and processing. This thesis presents a data-driven compression framework to reduce storage requirements for 3D simulation datasets. The proposed framework utilizes hypernetworks to dynamically generate coordinate-based networks’ weights, which are re sponsible for mapping spatial coordinates to fluid flow properties. This approach efficiently represents fluid dynamics, eliminating the need to store extensive flow field datasets. The hypernetwork generates these weights using compact latent codes tailored for each simulation configuration. By storing only the weights of the hypernetwork and the coordinate-based networks, the storage overhead is reduced. A key feature in the framework is adaptive inference, which allows the model to adapt to new CFD data with minimal fine-tuning. The latent codes generated for new simulation data enable rapid adaptation, ensuring the model remains scalable and efficient for diverse fluid dynamics data. In conclusion, the proposed framework offers a storage-efficient and flexible solution for handling large-scale CFD simulations. By combining compression with dynamic adaptability, this approach reduces computational and storage costs, providing a scalable method to improve the efficiency of fluid dynamics simulations across various fields.     «

Fluid dynamics simulations play a crucial role in analyzing the behavior of fluids in a wide range of scientific and engineering applications, including aerospace and automotive design. However, Computational Fluid Dynamicss (CFDs) demands substantial computational resources for storage and processing. This thesis presents a data-driven compression framework to reduce storage requirements for 3D simulation datasets. The proposed framework utilizes hypernetworks to dynamically generate coordinate...     »