Using neural networks for parameter estimation in ground water

By L. García, Abdalla M. T. Shigidi. 2006

103 citations recorded by the source corpus.

Subfields: Environmental Engineering, Geophysics, Ocean Engineering

Citations in the corpus

• fPINNs: Fractional Physics-Informed Neural Networks (2018, neutral): “The first category exploits only the information of observed data in the spatio-temporal domain and employs surrogate models such as Gaussian process regression (GP regression) [30], stochastic collocation methods [49, 50], and feedforward neural networks (NNs) [9, 11] to approximate the mapping fro”