pith:XJGAZGC3
Stochastic Smoothed Particle Hydrodynamics for Stochastic Mechanics Problems
Stochastic SPH represents uncertainties via polynomial chaos and Karhunen-Loève expansions to turn stochastic PDEs into coupled deterministic ODEs solved on particles.
arxiv:2605.12540 v1 · 2026-05-08 · cs.CE
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Claims
S-SPH achieves up to three orders of magnitude reduction in computational cost relative to direct sampling approaches while demonstrating excellent agreement with Monte Carlo simulation statistics of mean and variance on benchmark problems.
That the chosen polynomial chaos order and Karhunen-Loève truncation are sufficient to capture the statistics of the target uncertainties without significant truncation error, and that the ghost-particle boundary treatment remains stable and accurate once the stochastic coefficients are introduced.
S-SPH extends mesh-free SPH to stochastic problems via polynomial chaos and KL expansions, delivering mean and variance statistics that match Monte Carlo at up to 1000 times lower cost on benchmark advection and Burgers flows.
References
Receipt and verification
| First computed | 2026-05-18T03:10:02.357933Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
ba4c0c985b6625b5a60952087603692762aac2fe51fcc89b7171b5cbd48915aa
Aliases
· · · · ·Agent API
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/XJGAZGC3MYS3LJQJKIEHMA3JE5 \
| 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())"
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Canonical record JSON
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