4 papers
Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps
Dimitris G Giovanis, Nikolaos Evangelou, Ioannis G Kevrekidis +1
We present a generative learning framework for probabilistic sampling based on an extension of the Probabilistic Learning on Manifolds (PLoM) approach, which is designed to generat…
Generative Learning for Slow Manifolds and Bifurcation Diagrams
Ellis R. Crabtree, Dimitris G. Giovanis, Nikolaos Evangelou +2
In dynamical systems characterized by separation of time scales, the approximation of so called ``slow manifolds'', on which the long term dynamics lie, is a useful step for model…
Comparing analytic and data-driven approaches to parameter identifiability: A power systems case study
Nikolaos Evangelou, Alexander M. Stankovic, Ioannis G. Kevrekidis +1
Parameter identifiability refers to the capability of accurately inferring the parameter values of a model from its observations (data). Traditional analysis methods exploit analyt…
Initializing LSTM internal states via manifold learning
Felix P. Kemeth, Tom Bertalan, Nikolaos Evangelou +3
We present an approach, based on learning an intrinsic data manifold, for the initialization of the internal state values of LSTM recurrent neural networks, ensuring consistency wi…