This paper benchmarks five distinct uncertainty quantification methods—including Delta, Bayesian Dropout, and Bootstrap—to determine the optimal approach for predicting turbine gas temperature degrada…

The paper introduces an LLM-driven framework to automatically standardize, structure, and enrich unstructured free-text wind turbine maintenance logs, transforming qualitative field observations into…

The paper introduces Picid, a modular evaluation infrastructure that standardizes and formalizes the entire Prognostics and Health Management (PHM) evaluation pipeline to ensure reproducible and fair…

EnergyMamba proposes an uncertainty-aware, graph-enhanced selective state space model to significantly improve both the accuracy and reliability of energy consumption prediction by explicitly modeling…

The paper demonstrates that Low-Rank Adaptation (LoRA) is an effective and superior method for adapting large, pretrained Transformer surrogates for automotive aerodynamics to new vehicle families usi…

The paper proposes an uncertainty-aware transfer learning framework using the Temporal Fusion Transformer (TFT) to achieve robust and scalable energy forecasting across different buildings, demonstrat…

The paper proposes PC-MambaSDE, a physically-constrained continuous-time framework that accurately predicts Remaining Useful Life (RUL) despite irregular sensor observations and ensures physically pla…

The paper introduces an agentic, framework-based system to transform under-specified academic papers into standardized, comparable, and executable benchmarks for industrial Prognostics and Health Mana…

This paper analyzes the multi-regime behavior of Scientific Machine Learning (SciML) models, finding that optimization effectiveness is regime-specific and that failure modes require a unified, regime…

The paper proposes a graph attention-based virtual metrology framework that accurately predicts film thickness in semiconductor deposition by modeling structured, directional dependencies among hetero…

The paper introduces 'dashi,' an open-source Python library that provides comprehensive tools for characterizing dataset shifts (covariate, prior, concept) to ensure robust and trustworthy AI developm…

The paper proposes VLBM, a latent basis modeling framework, to achieve state-of-the-art robustness in multivariate time series forecasting, particularly when facing rare but high-impact out-of-distrib…

IstGPT introduces a novel LLM-based framework for real-time, fine-grained anomaly detection in complex industrial cyber-physical systems, achieving state-of-the-art performance across multiple benchma…

The paper formalizes LLM degradation during long generation as 'cognitive fatigue' and introduces the Fatigue Index (FI), a measurable, model-agnostic diagnostic tool for real-time monitoring.

The paper proposes S3TS, a novel tree search algorithm that simultaneously handles both non-linear system models and explicit uncertainties (scenarios) for advanced energy planning, achieving near-opt…

The paper introduces a unified Physics-Informed Deep Learning (PIDL) framework that simultaneously enforces physical laws and information-theoretic bounds, demonstrating robust, domain-agnostic entrop…

The paper proposes TRACE, a trajectory risk-aware compression method, to effectively aggregate sparse and delayed safety evidence across long agent trajectories, achieving state-of-the-art performance…