arXiv (All)
open access
Robust topology optimization with non-Gaussian material fields using polygonal finite elements
and-sciencecomputationcomputational-engineeringfinancemethodologynumerical-analysisoptimization-and-control
We present a computational framework for robust topology optimization that integrates polygonal finite-element discretizations, spatially correlated non-Gaussian material modeling, and non-intrusive polynomial-chaos surrogates. Spatial uncertainty in Young's modulus is represented as a homogeneous n
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Sensitivity Hot Spot Penalization: A Robust Topology Optimization Framework against First-Order Worst-Case Perturbations#799695
- Adjustable Robust Optimization with Discrete Uncertainty#796345
- Robust Resource Management for SAGIN using DNN-Driven Channel Uncertainty Learning#1128986
- Development of high fidelity methods for 3D Monte Carlo transient analysis of nuclear reactors#284707
- Robust Optimization Under Objective Functional Uncertainty#1011105
Record · ID 972530
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive