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Yang Yang

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author1
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • math.NA3
  • cs.CE1
same name
  • Yang Yang — 33 papers
  • Yang Yang — 18 papers
  • Yang Yang — 13 papers, h 19
  • Yang Yang — 13 papers
  • Yang Yang — 11 papers, h 44
  • Yang Yang — 10 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedA novel solution for seepage problems using physics-informed neural networks

4 citations · 4 across the 3 of their papers we have counts for

collaborators

4 papers

math.NA2025

Elasto-plastic cell-based smoothed finite element method solving geotechnical problems

Yang Yang, Mingjiao Yan, Zongliang Zhang +4

This work develops an elasto-plastic cell-based smoothed finite element method (CSFEM) for geotechnical analysis. The formulation incorporates a smoothed strain field into the stan…

math.NA2025

Three dimensional seepage analysis using a polyhedral scaled boundary finite element method

Mingjiao Yan, Yang Yang, Zongliang Zhang +3

This work presents a polyhedral scaled boundary finite element method (PSBFEM) for three dimensional seepage analysis. We first derive the scaled boundary formulation for 3D seepag…

math.NA2025

Steady-state and transient thermal stress analysis using a polygonal finite element method

Yang Yang, Mingjiao Yan, Zongliang Zhang +3

This work develops a polygonal finite element method (PFEM) for the analysis of steady-state and transient thermal stresses in two dimensional continua. The method employs Wachspre…

cs.CE2023★ 4 cited

A novel solution for seepage problems using physics-informed neural networks

Tianfu Luo, Yelin Feng, Qingfu Huang +5

A Physics-Informed Neural Network (PINN) provides a distinct advantage by synergizing neural networks' capabilities with the problem's governing physical laws. In this study, we in…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.