pith:N7CBNBCI
NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering
NeuroRisk embeds the Sort-and-Select structure of risk-aware traffic engineering into a neural unrolled optimizer to deliver solver accuracy at 100- to 100000-fold speedups.
arxiv:2605.12862 v1 · 2026-05-13 · cs.NI · cs.LG
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\pithnumber{N7CBNBCIG736WQ6RDNIDVGKR7R}
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Claims
NeuroRisk achieves small optimality gaps relative to the solver with orders of magnitude speedup (10^2-10^5 ×) on risk objectives, while outperforming neural baselines on nominal throughput.
That the Sort-and-Select structure can be faithfully embedded into a neural unrolled optimizer using gated edge-local reservations and permutation-invariant cues so that feasibility is enforced under explicit capacity constraints and scenario-dependent risk.
NeuroRisk is a physics-informed deep unrolled optimizer for risk-aware traffic engineering that achieves small optimality gaps and 100-100000x speedup over solvers while outperforming neural baselines on throughput.
References
Receipt and verification
| First computed | 2026-05-18T03:09:11.544809Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
6fc416844837f7eb43d11b503a9951fc6c5ae96c04d6446eb92ce27fbad35ae5
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/N7CBNBCIG736WQ6RDNIDVGKR7R \
| 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: 6fc416844837f7eb43d11b503a9951fc6c5ae96c04d6446eb92ce27fbad35ae5
Canonical record JSON
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