6 papers
When Rates Are Geometric: Rate-Certificate Transfer for Contact Splittings in Optimization
George A Kevrekidis
Discrete optimization algorithms are often analyzed through continuous-time limiting ODEs, but a convergence certificate for the ODE is not automatically one for the discrete algor…
Conformal Disentanglement and Latent-Space Curation: A Neural Framework for Perspective Synthesis, Differentiation and Targeted Generation
George A. Kevrekidis, Eleni D. Koronaki, Dimitris G. Giovanis +1
Many scientific and engineering problems involve observing a common phenomenon through multiple heterogeneous sensors or measurement modalities. Such observations typically contain…
Local Universal Splitting Integrators for Contact Hamiltonian Systems
George A Kevrekidis
Contact Hamiltonian systems extend symplectic Hamiltonian mechanics to dissipative settings while retaining geometric structure. We develop a structure-preserving splitting framewo…
Towards Coordinate- and Dimension-Agnostic Machine Learning for Partial Differential Equations
Trung V. Phan, George A. Kevrekidis, Soledad Villar +2
The machine learning methods for data-driven identification of partial differential equations (PDEs) are typically defined for a given number of spatial dimensions and a choice of…
Thinner Latent Spaces: Detecting Dimension and Imposing Invariance with Conformal Autoencoders
George A. Kevrekidis, Zan Ahmad, Mauro Maggioni +2
Conformal Autoencoders are a neural network architecture that imposes orthogonality conditions between the gradients of latent variables to obtain disentangled representations of d…
Data-Driven, ML-assisted Approaches to Problem Well-Posedness
Tom Bertalan, George A. Kevrekidis, Eleni D Koronaki +3
Classically, to solve differential equation problems, it is necessary to specify sufficient initial and/or boundary conditions so as to allow the existence of a unique solution. We…