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pith:2026:XSKRL2TYAMLHIHVSTVOVE5LJHV
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PI-SONet: A Physics-Informed Symplectic Operator Network for Real-Time Optimal Control of Multi-Agent Systems

Alan John Varghese, George Em Karniadakis, J\'er\^ome Darbon, Paula Chen, Shanqing Liu, Yaochen Zhu

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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Claims

C1strongest claim

PI-SONet achieves sub-second inferences on new problem instances, equating to up to 10,000x speedup over representative baselines.

C2weakest assumption

That a single trained conditional symplectic operator can reliably approximate the PMP solution map for unseen problem configurations while inherently preserving Hamiltonian structure.

C3one line summary

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

60 extracted · 60 resolved · 3 Pith anchors

[1] Math´ ematiques Concr` etes 2005
[2] John Wiley & Sons, Hoboken, NJ (2012) 2012 · doi:10.1002/9781118122631
[3] Foderaro, G., Ferrari, S., Wettergren, T.A.: Distributed optimal control for multi-agent trajectory optimization. Automatica50(1), 149–154 (2014) 2014
[4] IEEE Robotics and Automation Letters3(2), 1215–1222 (2018) 2018
[5] In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 2020

Formal links

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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

arxiv: 2605.14332 · arxiv_version: 2605.14332v1 · doi: 10.48550/arxiv.2605.14332 · pith_short_12: XSKRL2TYAMLH · pith_short_16: XSKRL2TYAMLHIHVS · pith_short_8: XSKRL2TY
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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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