103 citations · 682 across the 48 of their papers we have counts for
5 papers · 1 filter
Data-driven Control of Agent-based Models: an Equation/Variable-free Machine Learning Approach
Dimitrios G. Patsatzis, Lucia Russo, Ioannis G. Kevrekidis +1
We present an Equation/Variable free machine learning (EVFML) framework for the control of the collective dynamics of complex/multiscale systems modelled via microscopic/agent-base…
Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data
Seungjoon Lee, Yorgos M. Psarellis, Constantinos I. Siettos +1
We propose a machine learning framework for the data-driven discovery of macroscopic chemotactic Partial Differential Equations (PDEs) -- and the closures that lead to them -- from…
Parsimonious Physics-Informed Random Projection Neural Networks for Initial-Value Problems of ODEs and index-1 DAEs
Gianluca Fabiani, Evangelos Galaris, Lucia Russo +1
We address a physics-informed neural network based on the concept of random projections for the numerical solution of IVPs of nonlinear ODEs in linear-implicit form and index-1 DAE…
Constructing coarse-scale bifurcation diagrams from spatio-temporal observations of microscopic simulations: A parsimonious machine learning approach
Evangelos Galaris, Gianluca Fabiani, Ioannis Gallos +2
We address a three-tier data-driven approach to solve the inverse problem in complex systems modelling from spatio-temporal data produced by microscopic simulators using machine le…
Designing Social Distancing Policies for the COVID-19 Pandemic: A probabilistic model predictive control approach
Antonis Armaou, Bryce Katch, Lucia Russo +1
The effective control of the COVID-19 pandemic is one the most challenging issues of nowadays. The design of optimal control policies is perplexed from a variety of social, politic…