pith:XSKRL2TY
PI-SONet: A Physics-Informed Symplectic Operator Network for Real-Time Optimal Control of Multi-Agent Systems
A single trained conditional symplectic operator approximates the PMP solution map for families of high-dimensional optimal control problems and delivers sub-second inferences on new instances.
arxiv:2605.14332 v1 · 2026-05-14 · math.OC
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\usepackage{pith}
\pithnumber{XSKRL2TYAMLHIHVSTVOVE5LJHV}
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Record completeness
Claims
PI-SONet achieves sub-second inferences on new problem instances, equating to up to 10,000x speedup over representative baselines.
That a single trained conditional symplectic operator can reliably approximate the PMP solution map for unseen problem configurations while inherently preserving Hamiltonian structure.
PI-SONet trains a single structure-preserving operator network to deliver sub-second approximations to Pontryagin Maximum Principle solutions for parameterized multi-agent optimal control problems.
References
Formal links
Receipt and verification
| First computed | 2026-05-17T23:39:08.281678Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
bc9515ea780316741eb29d5d5275693d60dd288d38234c8cf9eaee84b7bb69d6
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/XSKRL2TYAMLHIHVSTVOVE5LJHV \
| 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: bc9515ea780316741eb29d5d5275693d60dd288d38234c8cf9eaee84b7bb69d6
Canonical record JSON
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"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "math.OC",
"submitted_at": "2026-05-14T03:55:39Z",
"title_canon_sha256": "7a25845de548c8bf27b7ba06d96cc46780a782174e07f136fd7e2541920f61ab"
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"source": {
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