2019-09-15
From structured data to evolution linear partial differential equations
Publication
Publication
Journal of Computational Physics
,
Volume 393
p. 162-
185
This paper is devoted to the derivation of computational methods for constructing partial differential equations from data. Following some recent works [7,14,15,20], we propose a methodology based on symbolic calculus [8,9,13], pseudospectral methods [2,3] and stochastic processes [6], in order to determine non-constant coefficients of linear evolution Partial Differential Equations (PDEs), from a set of structured data constituted by solutions at given times and positions, of an unknown linear PDE.
| Additional Metadata | |
|---|---|
| Keywords | Inverse problems, Numerical approximation, Operator symbols, Partial differential equations, Pseudospectral methods |
| Persistent URL | dx.doi.org/10.1016/j.jcp.2019.04.049 |
| Journal | Journal of Computational Physics |
| Citation |
Lorin, E. (2019). From structured data to evolution linear partial differential equations. Journal of Computational Physics, 393, 162–185. doi:10.1016/j.jcp.2019.04.049
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