Finite-Volume Methods
By M. Schäfer. Computational Engineering - Introduction to Numerical Methods, 2021
965 citations recorded by the source corpus.
Subfields: Physical and Theoretical Chemistry
View Paper: https://www.semanticscholar.org/paper/41782d8aba134b902c83c8c0ec0b3446060b3d96
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In Physical and Theoretical Chemistry
- Wei Peng
- Ricardo Vinuesa
- Robert Eymard
- Thierry Gallouët
- Raphaèle Herbin
- Zixue Xiang
- Xu Liu
- Steven L. Brunton
Citations in the corpus
- Enhancing computational fluid dynamics with machine learning (2021, methodology): “Developing a reduced-order model involves (1) finding a set of reduced coordinates, typically describing the amplitudes of important flow structures, and (2) identifying a differential-equation model (that is, a dynamical system) for how these amplitudes evolve in time.”
- Self-adaptive loss balanced Physics-informed neural networks (2021, methodology): “Setting initial collection as [ε f ,εb] = [2,2].”
- Finite Volume Methods (2019, methodology): “[53]) that, if k ≤ (1− ζ) h L , the approximate solution uh,k, that is the solution defined by (23.”