pith:QZDECZM4
Inducing Permutation Invariant Priors in Bayesian Optimization for Carbon Capture and Storage Applications
A novel Gaussian Process kernel encodes permutation invariance to improve Bayesian optimization for well placement in carbon capture projects.
arxiv:2605.02409 v2 · 2026-05-04 · cs.LG
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Record completeness
Claims
our main contribution is a novel Gaussian Process kernel (GP-Perm) that encodes permutation invariance by comparing sets through a stable divergence between their induced empirical representations, and can be combined with standard kernels for additional vector-valued inputs.
That the group-control mode of the high-fidelity CCS simulator produces genuine permutation symmetries that standard GP kernels cannot exploit and that the proposed divergence-based comparison will yield measurable gains in optimization performance on the Johansen formation case.
A novel permutation-invariant GP kernel using set divergence is introduced for Bayesian optimization in CCS well placement and tested on synthetic benchmarks plus one real formation case.
Receipt and verification
| First computed | 2026-05-22T01:04:04.125162Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
864641659c61c32644a3c906cd298562ef6d8aca852eba846dceaa3caeac5b2c
Aliases
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/QZDECZM4MHBSMRFDZEDM2KMFML \
| 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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