13 citations · 37 across the 8 of their papers we have counts for
8 papers
A Bayesian-optimization framework coupling a multiphase PDE tumor model to efficiently design combination therapy schedules
Ioannis Lampropoulos, Yorgos Psarellis, Michail Kavousanakis
Designing combination cancer therapies requires choosing not only which agents to combine but also their relative doses and timing decisions that critically shape the trade-off bet…
Active search for Bifurcations
Yorgos M. Psarellis, Themistoklis P. Sapsis, Ioannis G. Kevrekidis
Bifurcations mark qualitative changes of long-term behavior in dynamical systems and can often signal sudden ("hard") transitions or catastrophic events (divergences). Accurately l…
Physics-agnostic and Physics-infused machine learning for thin films flows: modeling, and predictions from small data
Cristina P. Martin-Linares, Yorgos M. Psarellis, Georgios Karapetsas +2
Numerical simulations of multiphase flows are crucial in numerous engineering applications, but are often limited by the computationally demanding solution of the Navier-Stokes (NS…
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…
A note on the control of processes exhibiting input multiplicity
Robert J. Lovelett, Yorgos M. Psarellis, Ioannis G. Kevrekidis +1
Steady state multiplicity can occur in nonlinear systems, and this presents challenges to feedback control. Input multiplicity arises when the same steady state output values can b…
Data-driven Discovery of Chemotactic Migration of Bacteria via Machine Learning
Yorgos M. Psarellis, Seungjoon Lee, Tapomoy Bhattacharjee +3
E. coli chemotactic motion in the presence of a chemoattractant field has been extensively studied using wet laboratory experiments, stochastic computational models as well as part…
Limits of Entrainment of Circadian Neuronal Networks
Yorgos M. Psarellis, Michail Kavousanakis, Michael A. Henson +1
Circadian rhythmicity lies at the center of various important physiological and behavioral processes in mammals, such as sleep, metabolism, homeostasis, mood changes and more. It h…
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…