Research on intelligent design and synergistic optimization algorithms for photothermal performance in low-carbon building materials
低炭素建築材料における光熱性能のインテリジェント設計と相乗的最適化アルゴリズムに関する研究 (AI 翻訳)
Bin Li, Weilong Yang
🤖 gxceed AI 要約
日本語
本論文は、シンボリック回帰、強化学習、大規模言語モデルを組み合わせたAIフレームワークを開発し、低炭素建築材料の光熱性能最適化に適用した。都市微気候シミュレーションで検証し、表面温度調整や熱流束低減に顕著な改善を示した。計算効率も維持しながら、実時間適応性を実現した。
English
This paper develops an AI framework combining symbolic regression, reinforcement learning, and LLMs to optimize photothermal performance of low-carbon building materials. The method is validated in urban microclimate simulations, showing significant improvements in surface temperature regulation and heat flux reduction while maintaining computational efficiency and real-time adaptability.
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
本論文は、AIを活用した低炭素建材の設計最適化手法を示し、日本のZEB推進や都市ヒートアイランド対策に応用可能な知見を提供する。データ駆動型の材料設計は、日本の建築業界の脱炭素化に貢献しうる。
In the global GX context
This paper demonstrates how AI can accelerate the design of low-carbon building materials for urban environments, aligning with global efforts to reduce building-related emissions through smart materials and data-driven optimization. The framework's adaptability supports dynamic response to changing conditions, relevant for climate-resilient infrastructure.
👥 読者別の含意
🔬研究者:Provides a novel AI-driven methodology for materials design optimization with interpretable models and multi-objective trade-offs.
🏢実務担当者:Offers building designers and urban planners a framework to select and configure low-carbon materials for improved thermal performance and energy efficiency.
🏛政策担当者:Supports policies promoting smart city initiatives and energy-efficient building standards through data-driven material innovation.
📄 Abstract（原文）
This paper addresses the challenge of optimizing photothermal performance in low-carbon building materials under complex urban conditions. An integrated computational framework is developed, leveraging symbolic regression to construct interpretable models of material photothermal behavior and reinforcement learning to dynamically optimize design parameters in response to changing environmental inputs. The framework uses large language models for knowledge extraction and search space guidance, while leveraging evolutionary algorithms for multi-objective trade-offs between thermal efficiency and embodied carbon. Feature representation and computational tractability are improved thru principal component analysis and data normalization. The method is validated in urban microclimate simulation. The results show that the optimized material layout has significant improvements in surface temperature regulation and heat flux reduction compared with traditional materials. In addition to maintaining prediction accuracy and supporting real time adaptability, the system also achieved a large amount of computational savings. These results demonstrate the effectiveness of the proposed method in smart city environments and provide important information for future research in data-driven materials science and urban environmental engineering.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1117/12.3118404first seen 2026-07-13 05:45:04
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。