FiPy: Partial Differential Equations with Python
By J. Guyer, Daniel Wheeler, J. Warren. Computing in science & engineering (Print), 2009
393 citations recorded by the source corpus.
Subfields: Computational Mechanics, Materials Chemistry, Numerical Analysis
View Paper: https://www.semanticscholar.org/paper/7dd1db3763bcfcb6fb79249d6b2247513bd2db34
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
In Materials Chemistry
In Numerical Analysis
- Sharmila Karumuri
- Thomas Günther
- Jitesh H. Panchal
- Rohit Tripathy
- Ilias Bilionis
- Carsten Rücker
- Florian M. Wagner
Citations in the corpus
- Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification (2018, methodology): “The solver is implemented in the Python library FiPy [69].”
- pyGIMLi: An open-source library for modelling and inversion in geophysics (2017, background): “…pyGIMLi for its free, fl exible, and cross-platform-compatible nature, which therefore makes it widely used in the (geo)scienti fi c community (e.g., Guyer et al., 2009; Logg and Wells, 2010; P (cid:1) erez et al., 2011; Wellmann et al., 2012; Uieda et al., 2013; Cockett et al., 2015; Weigand and…”
- Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (2019, methodology): “(44)) are evaluated at the cell centers and fed as input to the FiPy solver.”