Pith Number

pith:B5PBNIS5

pith:2026:B5PBNIS5KJMK4Q24VVJ7L7FJ6R

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A Hybrid Optimization Framework for Spatial Packaging of Interconnected Systems

S. Westerhof, T. Hofman

A hybrid optimization framework for spatial packaging of interconnected systems achieves over 10% improvement and 0.6-2% accuracy to analytical optima.

arxiv:2605.17424 v1 · 2026-05-17 · cs.CE · cs.MS

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Claims

C1strongest claim

Results indicate that the presented method achieves more than a 10% improvement over existing SPI2 implementations and converges to spatially analytical optima across various benchmark scenarios. Benchmark experiments show solution accuracy of 0.6-2% relative to the ground truth.

C2weakest assumption

That isolating the spatial optimization aspect from physical interactions allows independent and meaningful evaluation of placement and routing performance, as stated in the abstract when describing the study focus.

C3one line summary

A hybrid optimization framework using maximal disjoint ball decomposition and interior point methods achieves over 10% improvement and 0.6-2% accuracy relative to ground truth on spatial packaging benchmarks.

References

43 extracted · 43 resolved · 1 Pith anchors

[1] Y. Haga, T. Matsunaga, T. Kobayashi, and N. Tsuruoka, ‘Min- imally Invasive Medical Devices and Healthcare Devices Using Microfabrication Technology’, in Advanced Mechatronics and MEMS Devices II, D. 2017 · doi:10.1007/978-3-319-32180-6

[2] D. V. Kladko and V. V. Vinogradov, ‘Magnetosurgery: Princi- ples, design, and applications’, Smart Materials in Medicine, vol. 5, no. 1, pp. 24–35, Mar. 2024, doi: 10.1016/j.smaim.2023.06.008 2024 · doi:10.1016/j.smaim.2023.06.008

[3] J. Van Kampen, M. Salazar, and T. Hofman, ‘A Two-dimensional Spatial Optimization Framework for Vehicle Powertrain Sys- tems’, in 2023 IEEE Vehicle Power and Propulsion Confer- ence (VPPC), Milan, Ita 2023 · doi:10.1109/vppc60535.2023.10403195

[4] M. Hofstetter, M. Hirz, M. Gintzel, and A. Schmidhofer, ‘Multi- Objective System Design Synthesis for Electric Powertrain De- velopment’, in 2018 IEEE Transportation Electrification Confer- ence and E 2018 · doi:10.1109/itec.2018.8450113

[5] R. Cheng, Z. Zhang, L. Jia, X. Zhang, and L. Yang, ‘Com- ponent matching optimization of high-performance turbofan engine based on experiment test and adaptive performance model to control the average 2025 · doi:10.1016/j.jppr.2025.02.001

Receipt and verification

First computed	2026-05-20T00:03:57.812448Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0f5e16a25d5258ae435cad53f5fca9f45e710fd2fe6f7541ab25da8017e9f5d8

Aliases

arxiv: 2605.17424 · arxiv_version: 2605.17424v1 · doi: 10.48550/arxiv.2605.17424 · pith_short_12: B5PBNIS5KJMK · pith_short_16: B5PBNIS5KJMK4Q24 · pith_short_8: B5PBNIS5

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/B5PBNIS5KJMK4Q24VVJ7L7FJ6R \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 0f5e16a25d5258ae435cad53f5fca9f45e710fd2fe6f7541ab25da8017e9f5d8

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "46b4fc8c9ea9ec4d6a4cc56ce1cf7483c6b64317577ba0011ac2c4c63aa721c4",
    "cross_cats_sorted": [
      "cs.MS"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.CE",
    "submitted_at": "2026-05-17T12:39:05Z",
    "title_canon_sha256": "8fce842f8c38d1b64a1fb9c97c69863f33ef8552c664d2600d520a929f86716e"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.17424",
    "kind": "arxiv",
    "version": 1
  }
}