Pith. sign in

REVIEW 2 cited by

Analytical Benchmark Problems for Multifidelity Optimization Methods

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2204.07867 v1 pith:NHNGMMQW submitted 2022-04-16 math.OC cs.CE

L. Mainini , A. Serani , M. P. Rumpfkeil , E. Minisci , D. Quagliarella , H. Pehlivan , S. Yildiz , S. Ficini
show 6 more authors
R. Pellegrini F. Di Fiore D. Bryson M. Nikbay M. Diez P. Beran
This is my paper
classification math.OCcs.CE
keywords multifidelityanalyticalassessmentbenchmarkmethodsoptimizationproblemsaddition
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The paper presents a collection of analytical benchmark problems specifically selected to provide a set of stress tests for the assessment of multifidelity optimization methods. In addition, the paper discusses a comprehensive ensemble of metrics and criteria recommended for the rigorous and meaningful assessment of the performance of multifidelity strategies and algorithms.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multi-Fidelity Stochastic Trust Region Method with Adaptive Sampling

    math.OC 2025-08 conditional novelty 6.0 of 10

    ASTRO-MFDF adaptively selects sample sizes and fidelity levels in a multi-fidelity stochastic trust-region method, showing faster convergence than ASTRO-DF and Nelder-Mead on Rosenbrock and inventory problems.

  2. Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy

    cs.AI 2025-05 unverdicted novelty 3.0 of 10

    A perspective arguing that inverse design in manufacturing improves when expert-guided problem definition, physics-informed ML, and LLM interfaces are combined.

Pith tools