Multi-scale fusion network: A new deep learning structure for elliptic interface problems
Applied Mathematical Modelling, ISSN: 0307-904X, Vol: 114, Page: 252-269
2023
- 8Citations
- 4Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
In this paper, we construct a novel multi-scale fusion network as a new deep learning structure to solve the elliptic interface problem. Compared with the results of the fully connected neural network and ResNet, the new multi-scale fusion network is shown to be able to better capture “sharp turns”, leading to the improved accuracy. Furthermore, its numerical solutions can preserve the C0 continuity of the solution while keeping the flux jumps passing through different interfaces, thus maintaining the physics of the differential equation. Then, as an application, the new method is applied to solve the three-dimensional Poisson-Boltzmann equations to calculate the electrostatic potential of immersed biomolecules. Numerical experiments demonstrate the effectiveness of our new method compared to the results obtained by the finite element method.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0307904X22004735; http://dx.doi.org/10.1016/j.apm.2022.10.006; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85140136447&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0307904X22004735; https://dx.doi.org/10.1016/j.apm.2022.10.006
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know