6 citations · 13 across the 14 of their papers we have counts for
14 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…
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…
Active search for Bifurcations
Yorgos M. Psarellis, Themistoklis P. Sapsis, Ioannis G. Kevrekidis
Bifurcations mark qualitative changes of long-term behavior in dynamical systems and can often signal sudden ("hard") transitions or catastrophic events (divergences). Accurately l…
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…
Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification
Dimitris G. Giovanis, Dimitrios Loukrezis, Ioannis G. Kevrekidis +1
In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform…