{"type":"ScholarlyArticle","title":"PhyRE-Net: A Physics-Anchored Network with Active Spatial Gating for Industrial Infrared Thermal Forecasting","authors":[{"name":"Shuotong Yang","slug":"shuotong-yang","given":"Shuotong","family":"Yang","affiliation":""},{"name":"Haitao Zhang","slug":"haitao-zhang","given":"Haitao","family":"Zhang","affiliation":""}],"abstract":"Spatiotemporal forecasting of infrared thermal fields is a critical technology for the predictive maintenance of industrial electrical control equipment. However, many existing methods are designed under index-based temporal representations and global regression objectives, which are not well aligned with schedule-driven, non-stationary industrial regimes and the long-tailed spatial distribution of safety-critical hotspots. To address these issues, this paper proposes PhyRE-Net, a physical-time-anchored network architecture featuring active spatial gating. Specifically, we first design the multi-period resolution rotary positional embeddings mechanism within the backbone, which injects absolute temporal features into attention geometry to align predicted phases with non-stationary production schedules. We then devise a reinforcement learning-driven spatial gating mechanism to dynamically modulate feature responses, actively shifting the computational focus from the dominant background to sparse but critical thermal anomalies. Extensive experiments on a large-scale real-world dataset covering seven types of heterogeneous electrical equipment show that PhyRE-Net achieves consistent improvements over strong baselines across all evaluated metrics. The source code is available at https://github.com/YST-10/PhyRE-Net.","doi":"10.24963/ijcai.2026/577","url":"https://ijcai.org/proceedings/2026/577","pdf":"https://ijcai.org/proceedings/2026/0577.pdf","markdown":"https://ijcai.org/proceedings/2026/577.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/577","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Machine Learning","track":"Main Track","number":577,"pages":{"first":5182,"last":5190},"keywords":["Machine Learning: Reinforcement learning","Machine Learning: Applications","Machine Learning: Time series and data streams"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}