Inducing Permutation Invariant Priors in Bayesian Optimization for Carbon Capture and Storage Applications
二酸化炭素回収・貯留用途におけるベイズ最適化の置換不変事前分布の誘導 (AI 翻訳)
Sofianos Panagiotis Fotias, Vassilis Gaganis
🤖 gxceed AI 要約
日本語
本論文は、二酸化炭素回収・貯留（CCS）プロジェクトにおける坑井配置最適化のために、置換不変なガウス過程カーネル（GP-Perm）を提案する。標準的なカーネルでは扱えない注入井と生産井のグループ内の置換対称性を活用し、実用的なCCSケーススタディで有効性を示した。
English
This paper proposes a novel Gaussian Process kernel (GP-Perm) that encodes permutation invariance for well placement optimization in Carbon Capture and Storage (CCS) projects. It exploits symmetries within injector and producer groups, which standard kernels cannot handle, and demonstrates effectiveness on a realistic CCS case study (Johansen formation).
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではCCSがGX戦略の重要要素であり、効率的な坑井配置は実用化に直結する。本手法はシミュレーションの高速化に貢献し、国内CCSプロジェクトへの応用が期待される。
In the global GX context
CCS is a critical technology for global decarbonization, and optimizing well placement reduces costs and improves storage efficiency. This method advances Bayesian optimization for complex engineering problems with permutation symmetries, applicable beyond CCS.
👥 読者別の含意
🔬研究者:The GP-Perm kernel provides a new tool for Bayesian optimization with permutation-invariant inputs, applicable to CCS and other engineering optimization problems.
🏢実務担当者:CCS project developers can use this method to optimize well placement under group control, reducing simulation costs and improving storage performance.
🏛政策担当者:Policymakers supporting CCS deployment may note that advanced optimization methods can lower project costs and accelerate commercial-scale demonstrations.
📄 Abstract（原文）
Bayesian Optimization is an iterative method, tailored to optimizing expensive black box objective functions. Surrogate models like Gaussian Processes, which are the gold standard in Bayesian Optimization, can be inefficient for inputs with permutation symmetries, as the most common kernels employed are better suited for vector inputs rather than unordered sets of items. Motivated by this issue, we turn to permutation invariant Bayesian Optimization for well placement in Carbon Capture and Storage projects. The high fidelity black box simulator is instructed to operate wells under group control, giving rise to permutation symmetries within injector and producer groups that cannot be exploited with standard GP kernels. In this work, 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. As a learned invariant baseline, we also consider a Deep Kernel Learning model (DKL-DS) using the Deep Sets architecture to learn a permutation-invariant embedding. We evaluate the proposed methodology across 8 use cases, comprising seven synthetic benchmarks and one realistic CCS case study (Johansen formation)
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.48550/arxiv.2605.02409first seen 2026-05-17 06:33:00 · last seen 2026-05-20 05:13:31
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