Zobrazeno 1 - 10
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pro vyhledávání: '"Coelho C. P."'
Fractional Differential Equations (FDEs) are essential tools for modelling complex systems in science and engineering. They extend the traditional concepts of differentiation and integration to non-integer orders, enabling a more precise representati
Externí odkaz:
http://arxiv.org/abs/2403.02737
Real-world systems are often formulated as constrained optimization problems. Techniques to incorporate constraints into Neural Networks (NN), such as Neural Ordinary Differential Equations (Neural ODEs), have been used. However, these introduce hype
Externí odkaz:
http://arxiv.org/abs/2403.02730
Hydropower plants play a pivotal role in advancing clean and sustainable energy production, contributing significantly to the global transition towards renewable energy sources. However, hydropower plants are currently perceived both positively as so
Externí odkaz:
http://arxiv.org/abs/2403.02821
Hydropower plants are crucial for stable renewable energy and serve as vital water sources for sustainable agriculture. However, it is essential to assess the current water management practices associated with hydropower plant management software. A
Externí odkaz:
http://arxiv.org/abs/2311.13293
The continuous dynamics of natural systems has been effectively modelled using Neural Ordinary Differential Equations (Neural ODEs). However, for accurate and meaningful predictions, it is crucial that the models follow the underlying rules or laws t
Externí odkaz:
http://arxiv.org/abs/2307.14940
Due to their dynamic properties such as irregular sampling rate and high-frequency sampling, Continuous Time Series (CTS) are found in many applications. Since CTS with irregular sampling rate are difficult to model with standard Recurrent Neural Net
Externí odkaz:
http://arxiv.org/abs/2307.05126
This work introduces Neural Chronos Ordinary Differential Equations (Neural CODE), a deep neural network architecture that fits a continuous-time ODE dynamics for predicting the chronology of a system both forward and backward in time. To train the m
Externí odkaz:
http://arxiv.org/abs/2307.01023
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