11 citations · 13 across the 9 of their papers we have counts for
9 papers
Machine learning models for Si nanoparticle growth in nonthermal plasma
Matt Raymond, Paolo Elvati, Jacob C. Saldinger +3
Nanoparticles (NPs) formed in nonthermal plasmas (NTPs) can have unique properties and applications. However, modeling their growth in these environments presents significant chall…
Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets
Matt Raymond, Jacob Charles Saldinger, Paolo Elvati +2
Extracting meaningful features from complex, high-dimensional datasets across scientific domains remains challenging. Current methods often struggle with scalability, limiting thei…
On sparse identification of complex dynamical systems: A study on discovering influential reactions in chemical reaction networks
Farshad Harirchi, Doohyun Kim, Omar Khalil +5
A wide variety of real life complex networks are prohibitively large for modeling, analysis and control. Understanding the structure and dynamics of such networks entails creating…
Antiviral Drug-Membrane Permeability: the Viral Envelope and Cellular Organelles
Changjiang Liu, Paolo Elvati, Angela Violi
To shorten the time required to find effective new drugs, like antivirals, a key parameter to consider is membrane permeability, as a compound intended for an intracellular target…
Spatial Dependence of Polycyclic Aromatic Compounds Growth in Counterflow Flames
Qi Wang, Paolo Elvati, Doohyun Kim +5
The formation mechanisms of aromatic compounds in flame are strongly influenced by the chemical and thermal history that leads to their formation. Indeed, the complex environments…
The Role of Molecular Properties on the Dimerization of Aromatic Compounds
Paolo Elvati, Kirk Turrentine, Angela Violi
Recent results have shown the presence and importance of oxygen chemistry during the growth of aromatic compounds, leading to the formation of oxygenated structures that have been…
A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks
Farshad Harirchi, Omar A. Khalil, Sijia Liu +3
In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network. This reduced set of react…
A New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks
Farshad Harirchi, Doohyun Kim, Omar A. Khalil +4
Chemical kinetic mechanisms can be represented by sets of elementary reactions that are easily translated into mathematical terms using physicochemical relationships. The schematic…
Fast exploration of chemical reaction networks
Paolo Elvati, Angela Violi
A variety of natural phenomena comprises a huge number of competing reactions and short-lived intermediates. Any study of such processes requires the discovery and accurate modelin…