The paper introduces PLSP (Pre-hoc Liminal Space Profiling), an anticipatory framework for predicting out-of-distribution (OOD) data before inference. It proposes a dataset‑independent metric called the CREDibility Score (CREDS) and introduces credibility curves and heat maps to analyze a model’s maximum credibility and behavior across datasets. Experiments on multiple datasets show that CREDS can improve model robustness to OOD prediction.
By Vipul Bansal, Himanshu Buckchash, Balasubramanian Raman, Deepak Dhungana
arXiv:2607. 02203v1 Announce Type: new Abstract: Operator learning has emerged as a powerful tool for modeling complex physical systems in functional spaces.
By Mojgan Alishiri, Amirhossein Arzani
arXiv:2606. 10877v1 Announce Type: new Abstract: Occlusion-based attribution methods provide an intuitive way to estimate feature importance by perturbing input features and measuring the resulting change in model output.
By Thodoris Lymperopoulos, Ioannis Kakogeorgiou, Denia Kanellopoulou
arXiv:2606. 03885v1 Announce Type: new Abstract: Feature attribution methods explain predictions by assigning importance scores to input features.
By Kieran A. Murphy, Shameen Shrestha
arXiv:2511. 01592v2 Announce Type: replace Abstract: Energy estimation is critical to impact identification on aerospace composites, where low-velocity impacts can induce internal damage that is undetectable at the surface.
By Nat\'alia Ribeiro Marinho, Richard Loendersloot, Frank Grooteman, Jan Willem Wiegman, Uraz Odyurt, Tiedo Tinga
arXiv:2606. 06156v1 Announce Type: new Abstract: Machine learning-based predictive emissions monitoring systems offer a practical alternative to direct emissions measurement, but their deployment across gas turbine fleets is challenging when emissions labels are available for only a small subset of assets.
By Rebecca Potts, Aiden Durrant, Rick Hackney, Georgios Leontidis
Virtual sensing, the estimation of hard-to-measure quantities from available sensor measurements, is a critical enabler for control and monitoring in cyber-physical systems. However, when sensors fail...
arXiv:2607. 05563v1 Announce Type: new Abstract: Interpretable explanation methods in Artificial Intelligence aim to uncover the underlying causes and their effects, enabling a deeper understanding of why a system behaves in a certain way under different inputs.
By Spyridon Evangelatos, Christos Diou, Georgios Th. Papadopoulos, Evangelos Markakis, Panagiotis Sarigiannidis
arXiv:2601. 03040v2 Announce Type: replace-cross Abstract: A fundamental requirement for full autonomy is the ability to sustain accurate navigation in the absence of external data, such as GNSS signals or visual information.
By Arup Kumar Sahoo, Itzik Klein
arXiv:2603. 25670v3 Announce Type: replace Abstract: Safety monitoring is essential for Cyber-Physical Systems (CPSs).
By John Ayotunde, Qinghua Xu, Guancheng Wang, Lionel C. Briand
arXiv:2512. 08499v3 Announce Type: replace-cross Abstract: Development of reliable and physically interpretable probabilistic frameworks for industrial prognostics remain nascent, and existing literature is often insensitive as inputs move away from the training manifold.
By Waleed Razzaq, Yun-Bo Zhao
arXiv:2602.07008v5 Announce Type: replace
Abstract: Reliable models should not only predict correctly, but also base their decisions on acceptable evidence. However, conventional supervised learning...
By Ruoyu Chen, Shangquan Sun, Xiaoqing Guo, Kangwei Liu, Sanyi Zhang, Zhangcheng Wang, Shiming Liu, Qunli Zhang, Wei Wang, Hua Zhang, Xiaochun Cao