13 citations · 33 across the 10 of their papers we have counts for
11 papers
Learning to Choose: An Empowerment-Guided Multi-Agent System with semantic communication for Adaptive Method Selection
Geremy Loachamín-Suntaxi, Robert Lazar, Dimitrios G. Giovanis +2
Automating scientific computing workflows requires more than generating executable code: autonomous systems must also select appropriate computational strategies, implement them fa…
Data-driven inverse uncertainty quantification: application to the Chemical Vapor Deposition Reactor Modeling
Geremy Loachamín, Eleni D. Koronaki, Dimitrios G. Giovanis +4
This study presents a Bayesian framework for (inverse) uncertainty quantification and parameter estimation in a two-step Chemical Vapor Deposition coating process using production…
CFD Modelling and Sensitivity-Guided Design of Silicon Filament CVD Reactors
G. P. Gakis, G. Loachamín-Suntaxi, I. G. Aviziotis +6
Filament-based chemical vapor deposition (CVD) for silicon (Si) coatings is often treated as a straightforward adaptation of planar deposition, just with a cylindrical substrate. B…
Implementing NLPs in industrial process modeling: Addressing Categorical Variables
Eleni D. Koronaki, Geremy Loachamin Suntaxi, Paris Papavasileiou +4
Important variables of processes are often categorical, i.e. names or labels representing, e.g. categories of inputs, or types of reactors or a sequence of steps. In this work, we…
On Learning what to Learn: heterogeneous observations of dynamics and establishing (possibly causal) relations among them
David W. Sroczynski, Felix Dietrich, Eleni D. Koronaki +4
Before we attempt to learn a function between two (sets of) observables of a physical process, we must first decide what the inputs and what the outputs of the desired function are…
Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis
Geremy Loachamín Suntaxi, Paris Papavasileiou, Eleni D. Koronaki +8
This work introduces a comprehensive approach utilizing data-driven methods to elucidate the deposition process regimes in Chemical Vapor Deposition (CVD) reactors and the interpla…
Integrating supervised and unsupervised learning approaches to unveil critical process inputs
Paris Papavasileiou, Dimitrios G. Giovanis, Gabriele Pozzetti +6
This study introduces a machine learning framework tailored to large-scale industrial processes characterized by a plethora of numerical and categorical inputs. The framework aims…
Nonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy
Eleni D. Koronaki, Luise F. Kaven, Johannes M. M. Faust +2
Polymer particle size constitutes a crucial characteristic of product quality in polymerization. Raman spectroscopy is an established and reliable process analytical technology for…
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…
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…