| Talk_id | Date | Speaker | Title |
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19625
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Friday 9/13
4:10 PM
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Jiangguo (James) Liu, Colorado State University
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Developing Finite Element Solvers for Poroelasticity in the Two-field Approach
- Jiangguo (James) Liu, Colorado State University
- Developing Finite Element Solvers for Poroelasticity in the Two-field Approach
- 09/13/2019
- 4:10 PM - 5:00 PM
- C304 Wells Hall
This talk presents results from our recent efforts for reviving the 2-field approach (fluid pressure and solid displacement) for numerically solving poroelasticity problems. We choose quadrilateral and hexahedral meshes for spatial discretization since they are equally flexible in accommodating complicated domain geometry but involve less unknowns, compared to simplicial meshes. The Darcy equation is solved for fluid pressure by the novel weak Galerkin finite element methods, which establish the discrete weak gradient and numerical velocity in the Arbogast-Correa spaces. The elasticity equation is solved for solid displacement by the enriched Lagrangian elements, which were motivated by the Bernardi-Raugel elements for Stokes flow. These two types of finite elements are coupled through the implicit Euler temporal discretization to solve poroelasticity. Numerical experiments on benchmarks will be presented to show that the new solvers are locking-free. Implementation on deal.II will be discussed also. This talk is based on a series of joint work with several collaborators.
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19633
|
Friday 10/11
4:10 PM
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Arvind Krishna Saibaba, North Carolina State University
|
Randomized algorithms for low-rank tensor decompositions
- Arvind Krishna Saibaba, North Carolina State University
- Randomized algorithms for low-rank tensor decompositions
- 10/11/2019
- 4:10 PM - 5:00 PM
- C304 Wells Hall
Many applications in data science and scientific computing require the working with large-scale datasets that are expensive to store and manipulate. These datasets have inherent multidimensional structure that can be exploited in order to efficiently compress and
store them in an appropriate tensor format. In recent years, randomized matrix methods have been used to efficiently and accurately compute low-rank matrix decompositions. Motivated by this success, we develop several randomized algorithms for compressing
tensor datasets in the Tucker format. We present probabilistic error analysis for our algorithms and numerical results on several datasets: synthetic test tensors, and realistic applications including the compression of facial image samples in the Olivetti database, and word counts in the Enron email dataset.
Joint work with Rachel Minster (NC State) and Misha Kilmer (Tufts)
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19626
|
Friday 10/18
4:10 PM
|
Steven Wise, University of Tennessee, Knoxville
|
TBD
- Steven Wise, University of Tennessee, Knoxville
- TBD
- 10/18/2019
- 4:10 PM - 5:00 PM
- C304 Wells Hall
No abstract available.
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19643
|
Friday 11/8
4:10 PM
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Kritika Singhal, Ohio State University
|
TBA
- Kritika Singhal, Ohio State University
- TBA
- 11/08/2019
- 4:10 PM - 5:00 PM
- C304 Wells Hall
No abstract available.
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19640
|
Friday 11/15
4:10 PM
|
Yao Yao, Georgia Tech
|
TBA
- Yao Yao, Georgia Tech
- TBA
- 11/15/2019
- 4:10 PM - 5:00 PM
- C304 Wells Hall
No abstract available.
|
