4 papers
On Some Tunable Multi-fidelity Bayesian Optimization Frameworks
Arjun Manoj, Anastasia S. Georgiou, Dimitris G. Giovanis +2
Multi-fidelity optimization employs surrogate models that integrate information from varying levels of fidelity to guide efficient exploration of complex design spaces while minimi…
Fast-Slow Neural Networks for Learning Singularly Perturbed Dynamical Systems
Daniel A. Serino, Allen Alvarez Loya, Joshua W. Burby +2
Singularly perturbed dynamical systems play a crucial role in climate dynamics and plasma physics. A powerful and well-known tool to address these systems is the Fenichel normal fo…
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…
Generative Learning of Densities on Manifolds
Dimitris G. Giovanis, Ellis Crabtree, Roger G. Ghanem +1
A generative modeling framework is proposed that combines diffusion models and manifold learning to efficiently sample data densities on manifolds. The approach utilizes Diffusion…