Zobrazeno 1 - 10
of 1 516
pro vyhledávání: '"Almeida, Joao"'
Scientific Machine Learning is transforming traditional engineering industries by enhancing the efficiency of existing technologies and accelerating innovation, particularly in modeling chemical reactions. Despite recent advancements, the issue of so
Externí odkaz:
http://arxiv.org/abs/2408.10720
The recently introduced class of architectures known as Neural Operators has emerged as highly versatile tools applicable to a wide range of tasks in the field of Scientific Machine Learning (SciML), including data representation and forecasting. In
Externí odkaz:
http://arxiv.org/abs/2404.17535
Cooperative driving is an emerging paradigm to enhance the safety and efficiency of autonomous vehicles. To ensure successful cooperation, road users must reach a consensus for making collective decisions, while recording vehicular data to analyze an
Externí odkaz:
http://arxiv.org/abs/2401.13630
This study introduces a hybrid meta-heuristic for generating feasible course timetables in large-scale scenarios. We conducted tests using our university's instances. The current commercial software often struggles to meet constraints and takes hours
Externí odkaz:
http://arxiv.org/abs/2310.20334
Autor:
Chau, Minh Triet, Almeida, João Lucas de Sousa, Alhajjar, Elie, Junior, Alberto Costa Nogueira
A recent alternative for hydrogen transportation as a mixture with natural gas is blending it into natural gas pipelines. However, hydrogen embrittlement of material is a major concern for scientists and gas installation designers to avoid process fa
Externí odkaz:
http://arxiv.org/abs/2306.13116
Autor:
Almeida, João Lucas de Sousa, Pires, Arthur Cancellieri, Cid, Klaus Feine Vaz, Junior, Alberto Costa Nogueira
This work explores the physics-driven machine learning technique Operator Inference (OpInf) for predicting the state of chaotic dynamical systems. OpInf provides a non-intrusive approach to infer approximations of polynomial operators in reduced spac
Externí odkaz:
http://arxiv.org/abs/2206.01604
We address the thesis defence scheduling problem, a critical academic scheduling management process, which has been overshadowed in the literature by its counterparts, course timetabling and exam scheduling. Specifically, the single defence assignmen
Externí odkaz:
http://arxiv.org/abs/2205.07727
Publikováno v:
In Artificial Intelligence In Medicine October 2024 156
Autor:
Souza de Sousa, Camila Eduarda, Amaral Júnior, Francisco Paulo, Cardoso, Abmael da Silva, Ruggieri, Ana Cláudia, van Cleef, Flavia de Oliveira Scarpino, de Pádua, Fábio Teixeira, Almeida, João Carlos de Carvalho
Publikováno v:
In Applied Soil Ecology October 2024 202
Autor:
Barros, Ana Luisa, Raposo, Diogo, Almeida, João David, Jesus, Hugo, Oliveira, Maria Alexandra, Fernandes, Carlos Rodríguez, MacKenzie, Darryl I., Santos-Reis, Margarida
Publikováno v:
In Global Ecology and Conservation October 2024 54