Neural Network Technique in Some Inverse Problems of Mathematical Physics

By V. Gorbachenko, T. Lazovskaya, D. Tarkhov, A. Vasilyev, M. V. Zhukov. 2016

40 citations recorded by the source corpus.

Subfields: Applied Mathematics, Computer Vision and Pattern Recognition, Mathematical Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

1. G. Em Karniadakis
2. Guofei Pang

In Computer Vision and Pattern Recognition

1. Lu Lu
2. Guofei Pang
3. G. Em Karniadakis

In Mathematical Physics

1. G. Em Karniadakis
2. Lu Lu
3. Guofei Pang

Citations in the corpus

• fPINNs: Fractional Physics-Informed Neural Networks (2018, methodology): “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) [16], stochastic collocation methods [17], and feedforward neural networks (NNs) [18, 19] to approximate the mapping from ”