Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/87937
Título: | Prior knowledge meets neural odes: a two stage training method for improved explainability |
Autor(es): | Coelho, C. Costa, M. Fernanda P. Ferrás, Luís Jorge Lima |
Data: | 2023 |
Resumo(s): | Neural Ordinary Differential Equations (ODEs) have been used extensively to model physical systems because they represent a continuous-time function that can make predictions over the entire time domain. However, most of the time, the parameters of these physical systems are subject to strict laws/constraints. But there is no guarantee that the Neural ODE model satisfies these constraints. Therefore, we propose a two-stage training for Neural ODE. The first stage aims at find ing feasible parameters by minimizing a loss function defined by the constraints violation. The second stage aims at improving the feasible solution by minimiz ing the distance between the predicted and ground-truth values. By training the Neural ODE in two stages, we ensure that the governing laws of the system are satisfied and the model fits the data. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/87937 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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paper_revised.pdf | 498,03 kB | Adobe PDF | Ver/Abrir |