The paper introduces PetroBench, a comprehensive benchmark for evaluating Large Language Models across various domains of petroleum engineering, finding that models perform better on subjective tasks…

This paper introduces PETRA, a large-scale Petroleum Engineering Text for Retrieval Adaptation dataset and pipeline that converts noisy public web data into a curated domain corpus and synthetic super…

The study developed a comprehensive model to assess how hydrogen embrittlement affects pipeline defects, finding that hydrogen generally does not increase damage severity unless a passive dent is comb…

This paper improves the foundations of neural likelihood approximation for Bayesian inverse problems by making the learning problem strictly convex and showing convergence to the true likelihood.

This paper analyzes the statistical and computational limits of learning bounded linear operators between Sobolev spaces from noisy data, and constructs a finite-resolution blockwise least-squares est…

This paper analyzes the information security practices of Ugandan climate activists protesting the EACOP, finding that their daily lives are shaped by autonomous, multi-layered tactics designed to mit…

This paper describes the use of a trie data structure to enhance experiment efficiency in comparative pipeline experiments for cascading retrieval pipelines using PyTerrier, observing a 26% reduction…

This review surveys advanced techniques—including generative models, multimodal learning, and closed-loop workflows—for automated inverse materials design, enabling the targeted discovery of novel cry…

This paper introduces NOTES, a method for efficient and transferable inverse design of physical systems using neural operators, dimensionality reduction, and evolutionary optimization.

This paper proposes STOIC, a framework that integrates graph-based forecasting with tabular foundation models for uncertainty quantification in energy demand forecasting using spatial-temporal graph n…

The paper introduces History-Bootstrapped Flow Matching (HB-ARFM) to solve ill-posed spatiotemporal inverse problems, enabling the reconstruction of full physical fields from partial observations by l…

This perspective reviews advanced design principles for Metal-Organic Frameworks (MOFs) used in water harvesting and details how integrating Artificial Intelligence (AI) can accelerate the discovery o…

This paper explores tools for control engineers to design socio-technical systems in a more principled and ethical manner, using feedback optimization, control of Markov decision processes, and model…

This paper introduces Earthquaker-AI, a hybrid educational framework that combines robotics, rubrics, and AI to enhance earthquake preparedness in primary-school students.

The paper introduces a Jacobian-based spectral audit to evaluate neural operators, demonstrating that standard prediction error metrics fail to capture crucial local dynamical structures and operator…

The paper introduces Cellular Sheaf Neural Operators, a discretization-aware framework that models constrained PDEs by representing physical states on oriented cell complexes to enforce structure-pres…

This paper proposes an automated method to generate complete PDDL planning problems directly from Asset Administration Shell (AAS) capability models, eliminating the need for specialized planning expe…

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…