Name
Abstract | ||
|---|---|---|
An inverse elastic source problem with sparse measurements is our concern. A generic mathematical framework is proposed which extends a low-dimensional manifold regularization in the conventional source reconstruction algorithms thereby enhancing their performance with sparse data-sets. It is rigorously established that the proposed framework is equivalent to the so-called deep convolutional framelet expansion in machine learning literature for inverse problems. Apposite numerical examples are furnished to substantiate the efficacy of the proposed framework. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1137/18M1174027 | SIAM JOURNAL ON APPLIED MATHEMATICS |
Keywords | Field | DocType |
elasticity imaging,inverse source problem,deep learning,convolutional neural network,deep convolutional framelets,time-reversal | Inverse,Applied mathematics,Mathematical optimization,Inverse source problem,Convolutional neural network,Manifold regularization,Artificial intelligence,Deep learning,Elasticity (economics),Mathematics | Journal |
Volume | Issue | ISSN |
78 | 5 | 0036-1399 |
Citations | PageRank | References |
1 | 0.38 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae Jun Yoo | 1 | 157 | 9.48 |
| Abdul Wahab | 2 | 1 | 1.39 |
| Jong Chul Ye | 3 | 715 | 79.99 |