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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗5965 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Multiscale defect structure design and strength-ductility synergistic optimization of 6082 aluminum alloy based on machine learning and non-equilibrium processing ↗
6082 aluminum alloys are widely applied in lightweight structural fields, but their strength levels are relatively limited, and the difficulty in synergistically enhancing strength and ductility remains a key issue restricting the expansion of their high-end applications. To address this issue, this paper proposes an integrated research strategy combining machine learning (ML)-assisted composition design with high-energy ball milling-pulse forging (HEBM-PF) non-equilibrium processing. Under the premise of satisfying national standard composition constraints, target compositions with excellent…
- OpenAlex · Published · First archived
Fault Aware AI Based Load Scheduling for Industrial Facilities Using Machine Learning–Driven Demand Side Management ↗
Industrial demand side management (DSM) has become a key enabler for reducing electricity costs, mitigating peak demand, and improving renewable energy utilization. However, most existing DSM frameworks assume ideal operating conditions and neglect the impact of electrical disturbances, such as voltage deviations and supply outages, which are common in real industrial power systems. This paper proposes a fault aware artificial intelligence–based load scheduling framework for industrial facilities that explicitly integrates electrical disturbance states into the DSM decision making process. A c…
- OpenAlex · Published · First archived
A machine learning-based surrogate model for analyzing the nonlinear seismic response of steel moment frames with rotational friction dampers ↗
This study introduces a computational framework combining Non-Smooth Dynamics (NSD) and Machine Learning to optimize rotational friction dampers (RFDs) in steel moment-resisting frames. The NSD-LCP-PSOR method reformulates friction as a Linear Complementarity Problem, eliminating tuning parameters while preserving Coulomb’s law exactly. Approximately 434,000 nonlinear time–history simulations cover a factorial design space of 1000 shear-type frames of 3-20 stories and fundamental periods of 0.3-3.0 s, combined into 54,248 structure and damper configurations, each evaluated under eight real ear…
- OpenAlex · Published · First archived
Traceable Cybersecurity Conformity Pre-Assessment of Electric Vehicle Charging Equipment Using Cross-Standard Gap Analysis and a Local LLM ↗
Electric vehicle charging equipment now links vehicles, payment and roaming services, cloud platforms, and the grid, but its cybersecurity requirements are fragmented across standards. This study combines a cross-standard gap analysis with a traceable conformity pre-assessment framework for a local large language model; the framework is a design-validation proof of concept, not a production-ready conformity-assessment system. Using EN 18031 as the reference axis, IEC 62443-4-2, ETSI EN 303 645, and NIST IR 8473 requirements were mapped on a five-level correspondence scale by a single researche…
- OpenAlex · Published · First archived
Physics-informed explainable artificial intelligence framework for corrosion-fatigue life prediction, reliability assessment, and inspection planning of welded steel structures ↗
Atmospheric corrosion combined with cyclic loading is one of the most critical degradation mechanisms affecting the fatigue life of welded steel structures. Although numerous machine learning models have demonstrated the capability to accurately describe crack propagation processes, the direct application of these models to probabilistic analysis and life-cycle management often requires considerable computational effort. This study proposes a Physics-Informed Explainable Artificial Intelligence (XAI) Framework for corrosion-fatigue life prediction, reliability assessment, and inspection planni…
- OpenAlex · Published · First archived
Rethinking Attention: Entropy-Guided Iterative Refinement for Language Models ↗
Standard Multi-Head Attention (MHA) computes contextual representations in a single forward pass, providing no mechanism to detect or correct diffuse or suboptimal attention distributions. Existing efforts to improve attention have primarily targeted computational efficiency or sequence length, leaving attention quality itself largely unaddressed. We propose IMHA (Entropy-Guided Iterative Multi-Head Attention), a within-layer attention formulation that progressively refines token representations over T iterations using shared projection weights. At each step, the Shannon entropy of each token’…
- OpenAlex · Published · First archived
Enhancing VR User Modelling with Pupillometry: An Eye-Tracking-Driven Machine Learning Approach ↗
This study investigated user experience during artwork exploration in immersive virtual reality using eye-tracking and pupillometry data. The central component of the research is the presentation of Bitscope, an innovative application developed using UNITY technology. Preliminary findings suggest that the order of exposure to stationary and virtual reality (VR) environments may influence later visual attention and engagement. Large-scale data collected during VR sessions were used to develop machine-learning regression models for pupil-diameter variables. The models achieved high predictive pe…
- OpenAlex · Published · First archived
Exact Solutions of Higher-Dimensional Integrable Models Using Extended Trial Functions Within a Symbolic Neural Network Computation ↗
This paper investigates two integrable (2+1)-dimensional nonlinear evolution equations, namely the Kairat–II–X-extended (K–II–X-E) model and the Kairat–II-X (K–II–X)-type model, with the objective of deriving exact analytical soliton solutions. These models serve as fundamental prototypes for nonlinear wave dynamics and arise in a broad range of physical contexts, including fluid flows, optical signal propagation, plasma media, and selected biological systems. A (G′/G)-expansion neural networks (ENNs) framework is developed by rigorously coupling the algebraic mechanism of the classical method…
- OpenAlex · Published · First archived
Artificial intelligence capabilities and green innovation: The mediating role of digital production ↗
In recent years, academia and practitioners have become increasingly interested in artificial intelligence capabilities. Few empirical studies, however, have explored how artificial intelligence capability affects digital production and both exploratory and exploitative innovation. Survey data from 275 businesses across the manufacturing, technology, and service industries were analyzed by Partial Least Squares Structural Equation Modeling. The findings show that both exploratory and exploitative green innovation are significantly and positively associated with artificial intelligence capabili…
- OpenAlex · Published · First archived
Digital Twins as a Necessary Foundation for Deploying Physical AI on the Shop Floor ↗
Abstract Public discourse on Physical AI is dominated by a promise of full autonomy: machines that perceive, learn, and act on their own. This paper argues that, for the foreseeable future, the industrial shop floor will not be fully autonomous but hybrid, a coexistence of deterministically automated equipment, adaptive Physical AI systems, and human work. In this hybrid state, the decisive near-term challenge is not primarily the maturity of individual robotic components but the coordination of heterogeneous actors through a shared, system-spanning integration layer. We therefore argue a deli…
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How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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