OPTIMIZATION OF CONTINUOUS DATA ASSIMILATION REDUCED ORDER MODELS OF FLUID FLOW USING ARTIFICIAL INTELLIGENCE

Continuous data assimilation is a vital process in accurately representing and forecasting complex fluid flow phenomena. The computational cost associated with cad can be prohibitive when dealing with high-dimensional problems. to address this challenge, reduced order models have been introduced to approximate the full-order system with a lower-dimensional surrogate. this work focuses on the optimization of for fluid flow problems using artificial intelligence (AI) techniques. The objective of this study is to develop an efficient and accurate framework for CDA-ROMs by leveraging AI algorithms. the proposed approach aims to enhance the performance of CDA-ROMS by integrating AI-based techniques, such as deep learning and reinforcement learning. the use of deep learning enables the ROMs to learn complex patterns and relationships from the available data, facilitating accurate representation of the fluid flow dynamics. Reinforcement learning techniques are employed to optimize the assimilation process, seeking the most informative observations to update the ROM state, thereby improving the predictive capability of the ROM. a comprehensive dataset of fluid flow measurements is used to train the AIbased framework. the performance of the proposed approach is evaluated through various numerical experiments and comparisons with traditional CDA-ROM methods. the results demonstrate the effectiveness of the AI-based framework in reducing the computational cost of CDA while maintaining high accuracy in predicting fluid flow behavior. The optimized CDA-ROMs using AI techniques provide a promising avenue for real-time monitoring, control, and prediction of fluid flow systems. the integration of AI algorithms with CDA-ROMS opens up new opportunities for accurate and efficient data assimilation in various applications, including weather forecasting, pollutant dispersion modelling and optimization of fluid flow processes. furthermore, the insights gained from this study can contribute to the broader field of AI-assisted optimization in scientific simulations and computational fluid dynamics.