activity
20172026
most citedPhysics-Informed Neural Nets for Control of Dynamical Systems

142 citations · 156 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

A novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images

João Pedro Matos-Carvalho, Laio Oriel Seman, Stefano Frizzo Stefenon +2

The inspection of electrical power line insulators is essential for ensuring grid reliability and preventing failures caused by damaged or degraded insulation components. In recent…

cs.NI2026

EOS-Bench: A Comprehensive Benchmark for Earth Observation Satellite Scheduling

Qian Yin, Jiaxing Li, Jiaqi Cheng +23

Earth observation satellite imaging scheduling is a challenging NP-hard combinatorial optimisation problem central to space mission operations. While next-generation agile Earth ob…

math.OC2025

A relax-fix-and-exclude algorithm for an MINLP problem with multilinear interpolations

Bruno Machado Pacheco, Pedro Marcolin Antunes, Eduardo Camponogara +4

This paper introduces a novel algorithm for Mixed-Integer Nonlinear Programming (MINLP) problems with multilinear interpolations of look-up tables. These problems arise when object…

cs.LG2023

Deep-learning-based Early Fixing for Gas-lifted Oil Production Optimization: Supervised and Weakly-supervised Approaches

Bruno Machado Pacheco, Laio Oriel Seman, Eduardo Camponogara

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…

cs.LG2023★ 2 cited

Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem

Bruno Machado Pacheco, Laio Oriel Seman, Cezar Antonio Rigo +3

This study investigates how to schedule nanosatellite tasks more efficiently using Graph Neural Networks (GNNs). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the go…

cs.LG2021★ 142 cited

Physics-Informed Neural Nets for Control of Dynamical Systems

Eric Aislan Antonelo, Eduardo Camponogara, Laio Oriel Seman +3

Physics-informed neural networks (PINNs) impose known physical laws into the learning of deep neural networks, making sure they respect the physics of the process while decreasing…

stat.AP2017★ 12 cited

Agent-based simulation of the learning dissemination on a Project-Based Learning context considering the human aspects

Laio Oriel Seman, Romeu Hausmann, Eduardo Augusto Bezerra

This work presents an agent-based simulation (ABS) of the active learning process in an Electrical Engineering course. In order to generate input data to the simulation, an active…