3 papers
nlin.CD2025
Real-time forecasting of chaotic dynamics from sparse data and autoencoders
Elise Ãzalp, Elise Özalp, Andrea Nóvoa +2
The real-time prediction of chaotic systems requires a nonlinear-reduced order model (ROM) to forecast the dynamics, and a stream of data from sensors to update the ROM. Data-drive…
cs.LG2024
Inferring stability properties of chaotic systems on autoencoders' latent spaces
Elise Ãzalp, Luca Magri
The data-driven learning of solutions of partial differential equations can be based on a divide-and-conquer strategy. First, the high dimensional data is compressed to a latent sp…
nlin.CD2024
Stability analysis of chaotic systems in latent spaces
Elise Ãzalp, Luca Magri
Partial differential equations, and their chaotic solutions, are pervasive in the modelling of complex systems in engineering, science, and beyond. Data-driven methods can find sol…