Qianying Cao

Abstract Time series forecasting is essential across domains such as healthcare, energy, and climate modeling. While models like LSTMs, GRUs, Transformers, and State-Space Models (SSMs) have become widely used, selecting the optimal architecture remains unclear. We propose an automated framework that systematically designs hybrid architectures by combining LSTM, GRU, attention, and SSM modules.

Computational studies on TAA have investigated the propensity of multiple localized perturbations in aortic mechanics and mechanobiology to initiate and propagate dilatations in the aorta, including loss of elastic fiber integrity [20,24,25], reduced collagen cross-linking [26,27], dysfunctional mechanosensing [4], and impaired mechanoregulation of collagen fibers [28].

George Em Karniadakis Division of Applied Mathematics, Brown University, Providence, RI, 02912 USA School of Engineering, Brown University, Providence, RI, 02912 USA Search for more papers by this author