Based Reduced Order Model

Based Reduced Order Models (ROMs) aim to efficiently simulate complex systems, such as those governed by partial differential equations (PDEs), by drastically reducing the dimensionality of the problem. Current research heavily utilizes deep learning architectures, including autoencoders, convolutional neural networks, and recurrent neural networks like LSTMs, often coupled with traditional methods like Proper Orthogonal Decomposition (POD), to create accurate and computationally inexpensive surrogate models. This approach is significant because it enables real-time control and optimization of high-dimensional systems, uncertainty quantification in inverse problems, and improved extrapolation capabilities beyond the training data, impacting fields like fluid dynamics, structural mechanics, and chemical kinetics.