4 papers
Emergent spaces for coupled oscillators
Thomas N. Thiem, Mahdi Kooshkbaghi, Tom Bertalan +2
In this paper we present a systematic, data-driven approach to discovering "bespoke" coarse variables based on manifold learning algorithms. We illustrate this methodology with the…
Coarse-scale PDEs from fine-scale observations via machine learning
Seungjoon Lee, Mahdi Kooshkbaghi, Konstantinos Spiliotis +2
Complex spatiotemporal dynamics of physicochemical processes are often modeled at a microscopic level (through e.g. atomistic, agent-based or lattice models) based on first princip…
Manifold Learning for Organizing Unstructured Sets of Process Observations
Felix Dietrich, Mahdi Kooshkbaghi, Erik M. Bollt +1
Data mining is routinely used to organize ensembles of short temporal observations so as to reconstruct useful, low-dimensional realizations of an underlying dynamical system. In t…
Manifold learning for parameter reduction
Alexander Holiday, Mahdi Kooshkbaghi, Juan M. Bello-Rivas +3
Large scale dynamical systems (e.g. many nonlinear coupled differential equations) can often be summarized in terms of only a few state variables (a few equations), a trait that re…