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researcher

Seid Korić

5 papers here

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

author position
  • middle author5

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CE3
  • cs.AI1
  • physics.comp-ph1
ORCID 0000-0002-7330-6401

identity via Semantic Scholar / OpenAlex

activity
20142024
most citedOn the use of graph neural networks and shape-function-based gradient computation in the deep energy method

48 citations · 73 across the 5 of their papers we have counts for

collaborators
Showing cs.CEShow all

3 papers · 1 filter

cs.CE2024

Advanced Deep Operator Networks to Predict Multiphysics Solution Fields in Materials Processing and Additive Manufacturing

Shashank Kushwaha, Jaewan Park, Seid Koric +3

Unlike classical artificial neural networks, which require retraining for each new set of parametric inputs, the Deep Operator Network (DeepONet), a lately introduced deep learning…

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…

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