Abstract Deep neural networks and other deep learning methods have very successfully been applied to the numerical approximation of high-dimensional nonlinear parabolic partial differential equations (PDEs), which are widely used in finance, engineering, and natural sciences. In particular, simulations indicate that algorithms based on deep learning overcome the curse of dimensionality in the numerical approximation…
Partial Differential Equations and Applications Template
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About the Partial Differential Equations and Applications format
Partial Differential Equations and Applications is a peer-reviewed journal published by Springer Nature, covering Advanced Mathematical Physics Problems, Nonlinear Partial Differential Equations, Advanced Mathematical Modeling in Engineering.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. Partial Differential Equations and Applications 12, 45–58 (2023).
Formats any DOI in Partial Differential Equations and Applications style. No sign-up. |
| Publishes research in | Advanced Mathematical Physics Problems Nonlinear Partial Differential Equations Advanced Mathematical Modeling in Engineering Stability and Controllability of Differential Equations Navier-Stokes equation solutions |
| ISSN | 2662-2963 |
| Citation impact (2-yr) | 0.82 |
| h-index | 14 |
| i10-index | 32 |
| Total citations | 1,333 |
| Article processing charge | $2,890 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | www.springer.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in Partial Differential Equations and Applications per year
Citation impact of Partial Differential Equations and Applications by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in Partial Differential Equations and Applications
Abstract We introduce a new family of numerical algorithms for approximating solutions of general high-dimensional semilinear parabolic partial differential equations at single space-time points. The algorithm is obtained through a delicate combination of the Feynman–Kac and the Bismut–Elworthy–Li formulas, and an approximate decomposition of the Picard fixed-point iteration with multilevel accuracy. The algorithm has been…
Abstract Optimal control of diffusion processes is intimately connected to the problem of solving certain Hamilton–Jacobi–Bellman equations. Building on recent machine learning inspired approaches towards high-dimensional PDEs, we investigate the potential of iterative diffusion optimisation techniques, in particular considering applications in importance sampling and rare event simulation, and focusing on problems without diffusion control, with…