25 citations · 49 across the 14 of their papers we have counts for
15 papers
A Mechanistic Analysis of Transformers for Dynamical Systems
Gregory Duthé, Nikolaos Evangelou, Wei Liu +2
Transformers are increasingly adopted for modeling and forecasting time-series, yet their internal mechanisms remain poorly understood from a dynamical systems perspective. In cont…
Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps
Dimitris G Giovanis, Nikolaos Evangelou, Ioannis G Kevrekidis +1
We present a generative learning framework for probabilistic sampling based on an extension of the Probabilistic Learning on Manifolds (PLoM) approach, which is designed to generat…
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…
Comparing analytic and data-driven approaches to parameter identifiability: A power systems case study
Nikolaos Evangelou, Alexander M. Stankovic, Ioannis G. Kevrekidis +1
Parameter identifiability refers to the capability of accurately inferring the parameter values of a model from its observations (data). Traditional analysis methods exploit analyt…
From Noise to Signal: Unveiling Treatment Effects from Digital Health Data through Pharmacology-Informed Neural-SDE
Samira Pakravan, Nikolaos Evangelou, Maxime Usdin +2
Digital health technologies (DHT), such as wearable devices, provide personalized, continuous, and real-time monitoring of patient. These technologies are contributing to the devel…
Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling
Nikolaos Evangelou, Tianqi Cui, Juan M. Bello-Rivas +2
We study the tipping point collective dynamics of an adaptive susceptible-infected-susceptible (SIS) epidemiological network in a data-driven, machine learning-assisted manner. We…
Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era
Eleni D. Koronaki, Nikolaos Evangelou, Cristina P. Martin-Linares +2
This study presents a collection of purely data-driven workflows for constructing reduced-order models (ROMs) for distributed dynamical systems. The ROMs we focus on, are data-assi…
Machine Learning for the identification of phase-transitions in interacting agent-based systems: a Desai-Zwanzig example
Nikolaos Evangelou, Dimitrios G. Giovanis, George A. Kevrekidis +2
Deriving closed-form, analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phas…
Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points
Gianluca Fabiani, Nikolaos Evangelou, Tianqi Cui +4
We present a machine learning (ML)-assisted framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale modeling, for (a) detecting tipp…
From partial data to out-of-sample parameter and observation estimation with Diffusion Maps and Geometric Harmonics
Eleni D. Koronaki, Nikolaos Evangelou, Yorgos M. Psarellis +2
A data-driven framework is presented, that enables the prediction of quantities, either observations or parameters, given sufficient partial data. The framework is illustrated via…
Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal Particles
Nikolaos Evangelou, Felix Dietrich, Juan M. Bello-Rivas +4
We construct a reduced, data-driven, parameter dependent effective Stochastic Differential Equation (eSDE) for electric-field mediated colloidal crystallization using data obtained…
Double Diffusion Maps and their Latent Harmonics for Scientific Computations in Latent Space
Nikolaos Evangelou, Felix Dietrich, Eliodoro Chiavazzo +3
We introduce a data-driven approach to building reduced dynamical models through manifold learning; the reduced latent space is discovered using Diffusion Maps (a manifold learning…
On the Parameter Combinations That Matter and on Those That do Not
Nikolaos Evangelou, Noah J. Wichrowski, George A. Kevrekidis +4
We present a data-driven approach to characterizing nonidentifiability of a model's parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diff…
Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning
Felix Dietrich, Alexei Makeev, George Kevrekidis +4
We identify effective stochastic differential equations (SDE) for coarse observables of fine-grained particle- or agent-based simulations; these SDE then provide useful coarse surr…
Initializing LSTM internal states via manifold learning
Felix P. Kemeth, Tom Bertalan, Nikolaos Evangelou +3
We present an approach, based on learning an intrinsic data manifold, for the initialization of the internal state values of LSTM recurrent neural networks, ensuring consistency wi…