12 citations · 48 across the 21 of their papers we have counts for
9 papers · 1 filter
Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events
Isabella S. Thiel, Juan Bello-Rivas, Yannis G. Kevrekidis +1
Extreme events in chaotic systems are difficult to learn from short trajectories because they are controlled by transient finite-time instability rather than by frequently observed…
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…
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…
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…
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…
A Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN)
Jennifer Sleeman, David Chung, Anand Gnanadesikan +10
We propose a new Tipping Point Generative Adversarial Network (TIP-GAN) for better characterizing potential climate tipping points in Earth system models. We describe an adversaria…
Identifying Equivalent Training Dynamics
William T. Redman, Juan M. Bello-Rivas, Maria Fonoberova +3
Study of the nonlinear evolution deep neural network (DNN) parameters undergo during training has uncovered regimes of distinct dynamical behavior. While a detailed understanding o…
An Operator Theoretic View on Pruning Deep Neural Networks
William T. Redman, Maria Fonoberova, Ryan Mohr +2
The discovery of sparse subnetworks that are able to perform as well as full models has found broad applied and theoretical interest. While many pruning methods have been developed…
Applications of Koopman Mode Analysis to Neural Networks
Iva Manojlović, Maria Fonoberova, Ryan Mohr +4
We consider the training process of a neural network as a dynamical system acting on the high-dimensional weight space. Each epoch is an application of the map induced by the optim…