Latent Evolution

Latent evolution focuses on learning and modeling the underlying dynamics of complex systems by representing their evolution in a lower-dimensional latent space. Current research emphasizes using deep learning architectures, such as autoencoders and recurrent neural networks (like LSTMs), often coupled with techniques for uncertainty quantification, to efficiently capture and predict system behavior, including for both forward and inverse problems. This approach offers significant speedups compared to traditional methods for tasks like simulating partial differential equations or reconstructing upstream states from downstream measurements in particle beam dynamics, impacting fields ranging from accelerator physics to fluid dynamics. The ability to accurately model and predict latent evolution improves the efficiency and robustness of simulations and enables better understanding of complex systems.