Constructing coarse-scale bifurcation diagrams from spatio-temporal
observations of microscopic simulations: A parsimonious machine learning
approach

Constructing coarse-scale bifurcation diagrams from spatio-temporal observations of microscopic simulations: A parsimonious machine learning approach

31 January 2022

Evangelos Galaris

Gianluca Fabiani

Ioannis G. Kevrekidis

Constantinos Siettos

Papers citing "Constructing coarse-scale bifurcation diagrams from spatio-temporal observations of microscopic simulations: A parsimonious machine learning approach"

17 / 17 papers shown

Title
Enabling Local Neural Operators to perform Equation-Free System-Level Analysis Gianluca Fabiani H. Vandecasteele S. Goswami Constantinos Siettos Ioannis G. Kevrekidis 50 0 0 05 May 2025
GoRINNs: Godunov-Riemann Informed Neural Networks for Learning Hyperbolic Conservation Laws Dimitrios G. Patsatzis Mario di Bernardo L. Russo Constantinos Siettos AI4CE 26 1 0 29 Oct 2024
Stability Analysis of Physics-Informed Neural Networks for Stiff Linear Differential Equations Gianluca Fabiani Erik Bollt Constantinos Siettos A. Yannacopoulos 26 0 0 27 Aug 2024
Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operator Xinghao Dong Chuanqi Chen Jin-Long Wu DiffM AI4CE 41 5 0 06 Aug 2024
Active search for Bifurcations Y. M. Psarellis T. Sapsis Ioannis G. Kevrekidis 20 0 0 17 Jun 2024
RandONet: Shallow-Networks with Random Projections for learning linear and nonlinear operators Gianluca Fabiani Ioannis G. Kevrekidis Constantinos Siettos A. Yannacopoulos 14 10 0 08 Jun 2024
Solving partial differential equations with sampled neural networks Chinmay Datar Taniya Kapoor Abhishek Chandra Qing Sun Iryna Burak Erik Lien Bolager Anna Veselovska Massimo Fornasier Felix Dietrich 35 1 0 31 May 2024
A physics-informed neural network method for the approximation of slow invariant manifolds for the general class of stiff systems of ODEs Dimitrios G. Patsatzis Lucia Russo Constantinos Siettos PINN 18 1 0 18 Mar 2024
Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling N. Evangelou Tianqi Cui J. M. Bello-Rivas Alexei Makeev Ioannis G. Kevrekidis 17 1 0 01 Nov 2023
Machine Learning for the identification of phase-transitions in interacting agent-based systems: a Desai-Zwanzig example N. Evangelou Dimitrios G. Giovanis George A. Kevrekidis G. Pavliotis Ioannis G. Kevrekidis 11 0 0 29 Oct 2023
AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification Nazanin Ahmadi Daryakenari Mario De Florio K. Shukla George Karniadakis 30 31 0 29 Sep 2023
Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points Gianluca Fabiani N. Evangelou Tianqi Cui J. M. Bello-Rivas Cristina P. Martin-Linares Constantinos Siettos Ioannis G. Kevrekidis 25 2 0 25 Sep 2023
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE Discovery Pongpisit Thanasutives Takashi Morita M. Numao Ken-ichi Fukui 17 2 0 20 Aug 2023
Sampling weights of deep neural networks Erik Lien Bolager Iryna Burak Chinmay Datar Q. Sun Felix Dietrich BDL UQCV 11 16 0 29 Jun 2023
Data-driven modelling of brain activity using neural networks, Diffusion Maps, and the Koopman operator I. Gallos Daniel Lehmberg Felix Dietrich Constantinos Siettos 12 7 0 24 Apr 2023
Data-driven Control of Agent-based Models: an Equation/Variable-free Machine Learning Approach Dimitrios G. Patsatzis Lucia Russo Ioannis G. Kevrekidis Constantinos Siettos 11 15 0 12 Jul 2022
Recurrent Neural Networks for Dynamical Systems: Applications to Ordinary Differential Equations, Collective Motion, and Hydrological Modeling Yonggi Park Kelum Gajamannage D. Jayathilake Erik Bollt AI4CE 4 18 0 14 Feb 2022