3 citations · 6 across the 15 of their papers we have counts for
17 papers
Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model
Ion Matei, Maksym Zhenirovskyy, Takuya Kurihana +4
Aerial wildfire suppression requires decisions about when, where, and how to deploy limited aircraft. We present an intervention-design framework built on a frozen hybrid convoluti…
Neural-Parameterized Cellular Automata for Wildfire Spread
Maksym Zhenirovskyy, Ion Matei, Rohit Vuppala +2
Traditional wildfire models rely on rigid, low-dimensional parameters and static fuel maps, frequently underpredicting fire spread. To address this weakness, we introduce a hybrid…
Differentiable Parameter Optimization for DAEs with State-Dependent Events
Ion Matei, Maksym Zhenirovskyy, Anthony Wong
Differential-algebraic equations (DAEs) with state-dependent events arise in systems whose continuous dynamics are constrained by algebraic equations and interrupted by mode change…
Automated BPMN Model Generation from Textual Process Descriptions: A Multi-Stage LLM-Driven Approach
Ion Matei, Maksym Zhenirovskyy, Praveen Kumar Menaka Sekar +1
Automatically reconstructing BPMN models from unstructured natural-language descriptions remains challenging due to heterogeneous modeling conventions, multilingual sources, and th…
Resilience Quantification and its Support for Operational Resilience
Ion Matei, Maksym Zhenirovskyy
We present a method to quantify a system's resilience capacity, i.e., the set of degradation magnitudes for which all functional requirements remain satisfied. These requirements c…
Ambiguity Detection and Elimination in Automated Executable Process Modeling
Ion Matei, Praveen Kumar Menaka Sekar, Maksym Zhenirovskyy +4
Automated generation of executable Business Process Model and Notation (BPMN) models from natural-language specifications is increasingly enabled by large language models. However,…
Automatic Generation of Executable BPMN Models from Medical Guidelines
Praveen Kumar Menaka Sekar, Ion Matei, Maksym Zhenirovskyy +4
We present an end-to-end pipeline that converts healthcare policy documents into executable, data-aware Business Process Model and Notation (BPMN) models using large language model…
AI Enhanced Control Engineering Methods
Ion Matei, Raj Minhas, Johan de Kleer +1
AI and machine learning based approaches are becoming ubiquitous in almost all engineering fields. Control engineering cannot escape this trend. In this paper, we explore how AI to…
A Controlled Mean Field Model for Chiplet Population Dynamics
Iman Nodozi, Abhishek Halder, Ion Matei
In micro-assembly applications, ensemble of chiplets immersed in a dielectric fluid are steered using dielectrophoretic forces induced by an array of electrode population. Generali…
An optimization-based approach to automated design
Ion Matei, Maksym Zhenirovskyy, John Maxwell +1
We propose a model-based, automated, bottom-up approach for design, which is applicable to various physical domains, but in this work we focus on the electrical domain. This bottom…
A Quantum Algorithm for Computing All Diagnoses of a Switching Circuit
Alexander Feldman, Johan de Kleer, Ion Matei
Faults are stochastic by nature while most man-made systems, and especially computers, work deterministically. This necessitates the linking of probability theory with mathematical…
2D Density Control of Micro-Particles using Kernel Density Estimation
Ion Matei, Johan de Kleer, Maksym Zhenirovskyy
We address the problem of 2D particle density control. The particles are immersed in dielectric fluid and acted upon by manipulating an electric field. The electric field is contro…
System Resilience through Health Monitoring and Reconfiguration
Ion Matei, Wiktor Piotrowski, Alexandre Perez +4
We demonstrate an end-to-end framework to improve the resilience of man-made systems to unforeseen events. The framework is based on a physics-based digital twin model and three mo…
Improving the Efficiency of Gradient Descent Algorithms Applied to Optimization Problems with Dynamical Constraints
Ion Matei, Maksym Zhenirovskyy, Johan de Kleer +1
We introduce two block coordinate descent algorithms for solving optimization problems with ordinary differential equations (ODEs) as dynamical constraints. The algorithms do not n…
Semi-linear Poisson-mediated Flocking in a Cucker-Smale Model
Christos N. Mavridis, Amoolya Tirumalai, John S. Baras +1
We propose a family of compactly supported parametric interaction functions in the general Cucker-Smale flocking dynamics such that the mean-field macroscopic system of mass and mo…
Interpretable machine learning models: a physics-based view
Ion Matei, Johan de Kleer, Christoforos Somarakis +2
To understand changes in physical systems and facilitate decisions, explaining how model predictions are made is crucial. We use model-based interpretability, where models of physi…
Hybrid modeling: Applications in real-time diagnosis
Ion Matei, Johan de Kleer, Alexander Feldman +2
Reduced-order models that accurately abstract high fidelity models and enable faster simulation is vital for real-time, model-based diagnosis applications. In this paper, we outlin…