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pith:45ZKNSCI

pith:2026:45ZKNSCI4PS2QLQGAYGRU3BTW7

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Phy2-ExposNet: A Physics-Informed Neural Network for EMF Exposure Mapping in Complex Urban Environments

Joe Wiart, Shanshan Wang, Shuangning Li, Yarui Zhang

Phy2-ExposNet maps urban EMF exposure by first enforcing two physical constraints then refining residuals with a transformer, cutting error and model size.

arxiv:2605.03207 v1 · 2026-05-04 · eess.SP

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Claims

C1strongest claim

Experiments demonstrate that the proposed method achieves lower estimation error while significantly reducing model complexity compared to existing approaches. It achieves around 15% relative error reduction over strong baselines, while using over 80% fewer parameters than conventional physics-informed models.

C2weakest assumption

The two unspecified physical constraints in the estimation stage correctly capture EMF propagation physics in complex urban geometries, and the transformer residual stage adds only genuine unmodeled effects without introducing new artifacts.

C3one line summary

Phy2-ExposNet combines physics-informed neural estimation with transformer refinement to map electromagnetic field exposure, cutting error by ~15% and parameters by >80% versus baselines.

References

25 extracted · 25 resolved · 0 Pith anchors

[1] S. Faye, R. Camino, G. Rziga, P. A. Sarvari, N. Al-Naffakh, J. C. Estrada-Jim ´enez, E. Pardo, and D. Khadraoui. A survey on EMF-aware mobile net- work planning.IEEE Access, 11:84568–84597, 2023 2023

[2] Guidelines for limiting exposure to electro- magnetic fields (100 kHz to 300 GHz).Health Phys., 118(5):483–524 2020

[3] Chan- nel knowledge maps for 6g wireless networks: Con- struction, applications, and future challenges 2025

[4] Overview of the first pathloss radio map prediction challenge.IEEE Open Journal of Signal Processing 2024

[5] Electromagnetic field expo- sure mapping: A taxonomy and survey.Computers 2025

Receipt and verification

First computed	2026-06-30T02:17:22.078803Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e772a6c848e3e5a82e06060d1a6c33b7cb884e1a83ea7946a9814fb09f5f698b

Aliases

arxiv: 2605.03207 · arxiv_version: 2605.03207v1 · doi: 10.48550/arxiv.2605.03207 · pith_short_12: 45ZKNSCI4PS2 · pith_short_16: 45ZKNSCI4PS2QLQG · pith_short_8: 45ZKNSCI

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/45ZKNSCI4PS2QLQGAYGRU3BTW7 \
  | 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: e772a6c848e3e5a82e06060d1a6c33b7cb884e1a83ea7946a9814fb09f5f698b

Canonical record JSON

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    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-04T22:53:17Z",
    "title_canon_sha256": "f54ebf3d88e2933b1f3ba02cdf96181370c08da7f39bc7664e6247da7f208e77"
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