Zobrazeno 1 - 10
of 389
pro vyhledávání: '"Camponogara, Eduardo"'
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-informed neural
Externí odkaz:
http://arxiv.org/abs/2406.03472
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. Mathematically, these
Externí odkaz:
http://arxiv.org/abs/2405.16267
Neural networks, while powerful, often lack interpretability. Physics-Informed Neural Networks (PINNs) address this limitation by incorporating physics laws into the loss function, making them applicable to solving Ordinary Differential Equations (OD
Externí odkaz:
http://arxiv.org/abs/2403.02289
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 repeatedly s
Externí odkaz:
http://arxiv.org/abs/2309.00197
Autor:
Pacheco, Bruno Machado, Seman, Laio Oriel, Rigo, Cezar Antonio, Camponogara, Eduardo, Bezerra, Eduardo Augusto, Coelho, Leandro dos Santos
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 out in orbi
Externí odkaz:
http://arxiv.org/abs/2303.13773
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 to craft th
Externí odkaz:
http://arxiv.org/abs/2301.12001
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 Computing strateg
Externí odkaz:
http://arxiv.org/abs/2211.17179
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 Toolkit (SCO
Externí odkaz:
http://arxiv.org/abs/2210.16896
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 consists of e
Externí odkaz:
http://arxiv.org/abs/2210.06913
Autor:
Camponogara, Eduardo, Müller, Eduardo Rauh, de Souza, Felipe Augusto, Castelan Carlson, Rodrigo, Seman, Laio Oriel
Publikováno v:
In Transportation Research Part C October 2024 167
