Neural network differential equation and plasma equilibrium solver.

By B. P. Milligen, V. Tribaldos, J. Jimenez. 1995

How researchers see this paper

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

1. A. Gholami
2. Ferran Alet
3. Michael W. Mahoney
4. Tomas Lozano-Perez
5. Sergey Levine
6. Maria Bauzá
7. Leslie Pack Kaelbling
8. Alberto Rodriguez

Citations in the corpus

• fPINNs: Fractional Physics-Informed Neural Networks (2018, background): “In particular, we concentrate on physics-informed neural networks (PINNs) [28, 29, 30, 1], which belong to the second aforementioned category.”
• Graph Element Networks: adaptive, structured computation and memory (2019, methodology): “…neural networks (Takeuchi & Kosugi, 1994; Ramuhalli et al., 2005) or Hopfield networks (Guo et al., 1999; Al Salameh & Al Zuraiqi, 2008) as well as meshfree methods to solve PDEs (Dissanayake & Phan-Thien, 1994; van Milligen et al., 1995; Leake, 2018; Long et al., 2018; Sirignano, 2018).”
• Characterizing possible failure modes in physics-informed neural networks (2021, background): “This idea has been introduced in different forms in the past (for instance [5, 18, 25, 28, 32]).”