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researcher

Seid Korić

2 papers here

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

author position
  • middle author2

Across the 2 of 2 papers where every author was matched, so the position is known.

fields
  • cs.CE1
  • physics.comp-ph1
ORCID 0000-0002-7330-6401

identity via Semantic Scholar / OpenAlex

most citedOn the use of graph neural networks and shape-function-based gradient computation in the deep energy method

48 citations · 71 across the 2 of their papers we have counts for

collaborators

3 papers

cs.CE2023★ 2 cited

Gyroid-like metamaterials: Topology optimization and Deep Learning

Asha Viswanath, Diab W Abueidda, Mohamad Modrek +3

Triply periodic minimal surface (TPMS) metamaterials characterized by mathematically-controlled topologies exhibit better mechanical properties compared to uniform structures. The…

cs.CE2022★ 48 cited

On the use of graph neural networks and shape-function-based gradient computation in the deep energy method

Junyan He, Diab Abueidda, Seid Koric +1

A graph neural network (GCN) is employed in the deep energy method (DEM) model to solve the momentum balance equation in 3D for the deformation of linear elastic and hyperelastic m…

physics.comp-ph2014★ 23 cited

Alya: Towards Exascale for Engineering Simulation Codes

Mariano Vazquez, Guillaume Houzeaux, Seid Koric +9

Alya is the BSC in-house HPC-based multi-physics simulation code. It is designed from scratch to run efficiently in parallel supercomputers, solving coupled problems. The target do…

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