Eduardo Camponogara
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View article: A trust region method for output-constrained reservoir optimization under geological uncertainty
A trust region method for output-constrained reservoir optimization under geological uncertainty Open
We present an algorithm addressing well-control reservoir optimization problems in realistic situations. Primarily, we consider cases where the reservoir simulator is treated as a black box and the derivative information is unavailable. Th…
View article: Physics-Informed Neural Networks for Control of Single-Phase Flow Systems Governed by Partial Differential Equations
Physics-Informed Neural Networks for Control of Single-Phase Flow Systems Governed by Partial Differential Equations Open
The modeling and control of single-phase flow systems governed by Partial Differential Equations (PDEs) present challenges, especially under transient conditions. In this work, we extend the Physics-Informed Neural Nets for Control (PINC) …
View article: Identifying Large-Scale Linear Parameter Varying Systems with Dynamic Mode Decomposition Methods
Identifying Large-Scale Linear Parameter Varying Systems with Dynamic Mode Decomposition Methods Open
Linear Parameter Varying (LPV) Systems are a well-established class of nonlinear systems with a rich theory for stability analysis, control, and analytical response finding, among other aspects. Although there are works on data-driven iden…
View article: Physics-Informed Echo State Networks for Modeling Controllable Dynamical Systems
Physics-Informed Echo State Networks for Modeling Controllable Dynamical Systems Open
Echo State Networks (ESNs) are recurrent neural networks usually employed for modeling nonlinear dynamic systems with relatively ease of training. By incorporating physical laws into the training of ESNs, Physics-Informed ESNs (PI-ESNs) we…
View article: Distributed ℓ<sub>0</sub> Sparse Aggregative Optimization
Distributed ℓ<sub>0</sub> Sparse Aggregative Optimization Open
Sparse convex optimization involves optimization problems where the decision variables are constrained to have a certain number of entries equal to zero. In this paper, we focus on the sparse version of the so-called aggregative optimizati…
View article: A modified derivative-free SQP-filter trust-region method for uncertainty handling: application in gas-lift optimization
A modified derivative-free SQP-filter trust-region method for uncertainty handling: application in gas-lift optimization Open
We propose an effective algorithm for black-box optimization problems without derivatives in the presence of output constraints. The proposed algorithm is illustrated using a realistic short-term oil production case with complex functions …
View article: Solving Differential Equations using Physics-Informed Deep Equilibrium Models
Solving Differential Equations using Physics-Informed Deep Equilibrium Models Open
This paper introduces Physics-Informed Deep Equilibrium Models (PIDEQs) for solving initial value problems (IVPs) of ordinary differential equations (ODEs). Leveraging recent advancements in deep equilibrium models (DEQs) and physics-infor…
View article: A GPU-Accelerated Bi-linear ADMM Algorithm for Distributed Sparse Machine Learning
A GPU-Accelerated Bi-linear ADMM Algorithm for Distributed Sparse Machine Learning Open
This paper introduces the Bi-linear consensus Alternating Direction Method of Multipliers (Bi-cADMM), aimed at solving large-scale regularized Sparse Machine Learning (SML) problems defined over a network of computational nodes. Mathematic…
View article: FloripaSat MILPs
FloripaSat MILPs Open
This dataset contains several instances of the Offline Nanosatellite Task Scheduling (ONTS) problem, based on the parameters of the FloripaSat-1 mission. Each instance (.json file) is paired with a (quasi-)optimal solution vector (_opt.npz…
View article: FloripaSat MILPs
FloripaSat MILPs Open
This dataset contains several instances of the Offline Nanosatellite Task Scheduling (ONTS) problem, based on the parameters of the FloripaSat-1 mission. Each instance (.json file) is paired with a (quasi-)optimal solution vector (_opt.npz…
View article: Deep-learning-based Early Fixing for Gas-lifted Oil Production Optimization: Supervised and Weakly-supervised Approaches
Deep-learning-based Early Fixing for Gas-lifted Oil Production Optimization: Supervised and Weakly-supervised Approaches Open
Maximizing oil production from gas-lifted oil wells entails solving Mixed-Integer Linear Programs (MILPs). As the parameters of the wells, such as the basic-sediment-to-water ratio and the gas-oil ratio, are updated, the problems must be r…
View article: Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model
Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model Open
The predictive maintenance of electrical machines is a critical issue for companies, as it can greatly reduce maintenance costs, increase efficiency, and minimize downtime. In this paper, the issue of predicting electrical machine failures…
View article: Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem
Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem Open
This study investigates how to schedule nanosatellite tasks more efficiently using Graph Neural Networks (GNNs). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the goal is to find the optimal schedule for tasks to be carried …
View article: Combining Robust Control and Machine Learning for Uncertain Nonlinear Systems Subject to Persistent Disturbances
Combining Robust Control and Machine Learning for Uncertain Nonlinear Systems Subject to Persistent Disturbances Open
This paper proposes a control strategy consisting of a robust controller and an Echo State Network (ESN) based control law for stabilizing a class of uncertain nonlinear discrete-time systems subject to persistent disturbances. Firstly, th…
View article: Vertex-based reachability analysis for verifying ReLU deep neural networks
Vertex-based reachability analysis for verifying ReLU deep neural networks Open
Neural networks achieved high performance over different tasks, i.e. image identification, voice recognition and other applications. Despite their success, these models are still vulnerable regarding small perturbations, which can be used …
View article: Optimal preventive policies for parallel systems using Markov decision process: application to an offshore power plant
Optimal preventive policies for parallel systems using Markov decision process: application to an offshore power plant Open
This work proposes a Markov Decision Process (MDP) model for identifying windows of opportunities to perform preventive maintenance for multi-unit parallel systems subject to a varying demand. The main contribution lies in proposing: (i) a…
View article: Optimizing Operations Sequencing and Demulsifier Injection in Offshore Oil Production Platforms
Optimizing Operations Sequencing and Demulsifier Injection in Offshore Oil Production Platforms Open
Short-term production optimization of oil assets concerns routing decisions and equipment control settings that induce optimal steady-state operations. Typically, the decisions are made on the scale of hours to days with the aim of maximiz…
View article: Derivative-Free Optimization With Proxy Models for Oil Production Platforms Sharing a Subsea Gas Network
Derivative-Free Optimization With Proxy Models for Oil Production Platforms Sharing a Subsea Gas Network Open
The deployment of offshore platforms for the extraction of oil and gas from subsea reservoirs presents unique challenges, particularly when multiple platforms are connected by a subsea gas network. In the Santos basin, the aim is to maximi…
View article: Investigation of Proper Orthogonal Decomposition for Echo State Networks
Investigation of Proper Orthogonal Decomposition for Echo State Networks Open
Echo State Networks (ESN) are a type of Recurrent Neural Network that yields promising results in representing time series and nonlinear dynamic systems. Although they are equipped with a very efficient training procedure, Reservoir Comput…
View article: Sparse Convex Optimization Toolkit: A Mixed-Integer Framework
Sparse Convex Optimization Toolkit: A Mixed-Integer Framework Open
This paper proposes an open-source distributed solver for solving Sparse Convex Optimization (SCO) problems over computational networks. Motivated by past algorithmic advances in mixed-integer optimization, the Sparse Convex Optimization T…
View article: Distributed Primal Outer Approximation Algorithm for Sparse Convex Programming with Separable Structures
Distributed Primal Outer Approximation Algorithm for Sparse Convex Programming with Separable Structures Open
This paper presents the Distributed Primal Outer Approximation (DiPOA) algorithm for solving Sparse Convex Programming (SCP) problems with separable structures, efficiently, and in a decentralized manner. The DiPOA algorithm development co…
View article: An Energy-Aware Task Scheduling for Quality-of-Service Assurance in Constellations of Nanosatellites
An Energy-Aware Task Scheduling for Quality-of-Service Assurance in Constellations of Nanosatellites Open
When managing a constellation of nanosatellites, one may leverage this structure to improve the mission’s quality-of-service (QoS) by optimally distributing the tasks during an orbit. In this sense, this research proposes an offline energy…
View article: Derivative-free trust region optimization for robust well control under geological uncertainty
Derivative-free trust region optimization for robust well control under geological uncertainty Open
A Derivative-Free Trust-Region (DFTR) algorithm is proposed to solve the robust well control optimization problem under geological uncertainty. Derivative-Free (DF) methods are often a practical alternative when gradients are not available…