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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 ↗5570 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
IL-FormAI: A machine learning-guided framework for screening mRNA-LNP formulations toward improved in vivo expression ↗
The mRNA delivery efficiency of lipid nanoparticles (LNPs) is governed by the chemical structure of ionizable lipid (IL) and formulation. Appropriate formulation optimization may help realize the delivery potential of IL, but current formulation screening methods for newly designed ILs still rely on wet-lab experiments by trial and error. Here, we present IL-FormAI, a machine learning-guided formulation optimization framework that rapidly generates candidate LNP formulations for ionizable lipids with diverse chemical structures. Briefly, we first set up an expanding database containing IL stru…
- OpenAlex · Published · First archived
A GraphRAG-Enhanced Multi-Agent Framework for Blast Design in Drill-And-Blast Tunnels ↗
Blast design in drill-and-blast tunnelling requires geological interpretation and parameter selection for each excavation cycle, yet these tasks are often conducted separately, making it difficult to adapt plans to changing ground conditions. This study develops a multi-agent framework that integrates multimodal large language models with graph-based retrieval-augmented generation (GraphRAG) to connect geological assessment with preliminary blast-plan generation. A geological analysis agent, adapted through low-rank adaptation (LoRA)-based supervised fine-tuning on 1222 training cycles, combin…
- OpenAlex · Published · First archived
Metaheuristic-Driven Machine Learning for Best-Observed Feasible Weight Estimation of Braced Steel High-Rise Frames ↗
Preliminary selection of bracing systems in high-rise steel buildings requires comparing many configurations, each demanding computationally intensive minimum-weight design. This study uses machine learning to estimate the best-observed feasible weight directly from building-level configuration parameters. A full factorial dataset of 45 configurations covered three storey numbers (17, 24, and 30), three bracing types (V, X, and K), and five brace-placement layouts. Each configuration was optimized in a coupled SAP2000–MATLAB procedure with Particle Swarm, Grey Wolf, Honey Badger, and Aquila op…
- OpenAlex · Published · First archived
DEVELOPING LEARNERS' THINKING BASED ON ARTIFICIAL INTELLIGENCE ↗
This article addresses the development of learners’ thinking abilities through the use of artificial intelligence technologies in the educational process. It analyzes the potential of artificial intelligence-based interactive tools in developing analytical, critical, creative, and independent thinking. In addition, the article identifies the pedagogical conditions for the effective use of artificial intelligence in developing learners’ skills in analyzing problem situations, drawing independent conclusions, asking questions, and substantiating their own viewpoints.
- OpenAlex · Published · First archived
Agentic AI for Computational Fluid Dynamics: A Review ↗
Computational fluid dynamics (CFD) workflows remain strongly dependent on expert decisions in geometry preparation, meshing, model and solver configuration, convergence assessment, post-processing, and design iteration. This review examines agentic artificial intelligence (AI) for CFD automation and engineering design, with emphasis on reinforcement learning (RL) agents and large language model (LLM) agents. RL agents provide rapid, task-specific, and physics-aware execution within predefined observation and action spaces, whereas LLM agents offer flexible goal interpretation, knowledge access…
- OpenAlex · Published · First archived
Unified Hermite polynomial collocation and artificial neural network methods to analyse viscoelastic-bioconvective fluid behavior in a circular porous slider system ↗
Abstract The present study investigates the transport properties of a viscoelastic Maxwell fluid over a circular porous slider under the combined impacts of quadratic thermal radiation, activation energy, chemical reaction and bioconvection. Similarity transformations are employed to reduce the governing equations for momentum, energy, concentration and micro-organism movement into a dimensionless system which is then solved using the Hermite Polynomial Collocation Method (HPCM). Furthermore, a trained artificial neural network (ANN) with Levenberg–Marquardt algorithm is applied for prediction…
- OpenAlex · Published · First archived
Physics-Informed Neural Network Surrogate Modeling of Three-Dimensional Gas–Liquid Hydrodynamics in Aerated Double-Impeller Stirred Tanks ↗
This study develops a physics-informed surrogate for predicting three-dimensional gas–liquid hydrodynamics in an aerated double-impeller stirred tank. Reference fields were generated using steady-state computational fluid dynamics (CFD) simulations in ANSYS Fluent at impeller speeds of 200–500 rpm. Three models with identical network architectures were examined: a supervised neural network (NN) trained on CFD data, a boundary-informed neural network (BINN) incorporating boundary constraints, and a physics-informed neural network (PINN) incorporating boundary and reduced physics constraints. At…
- OpenAlex · Published · First archived
Latent profiles of AI teaching self-efficacy and their associations with occupational well-being among vocational education teachers in China: the moderating role of AI anxiety ↗
The rapid diffusion of artificial intelligence (AI) tools has placed Chinese vocational education teachers at the frontline of a transition combining instructional, practical-training, and emotional demands. Although prior variable-centered research has linked teachers’ AI-related self-efficacy to occupational outcomes, heterogeneity in the multidimensional structure of AI teaching self-efficacy has not been systematically examined in vocational education, and the moderating role of AI anxiety in linking such configurations to occupational well-being remains untested. A regionally stratified o…
- OpenAlex · Published · First archived
Machine learning analysis for the thermophoretic particle deposition in casson nanofluid with porous medium and heat generation ↗
The aim of this work is to evaluate the impacts of heat generation on the radiative flow of Casson nanofluid over a sheet under LTNE circumstances while microbes are present, utilizing machine learning techniques based on the Levenberg-Marquardt algorithm. A simple method for moving tiny particles across a temperature gradient is thermophoretic particle deposition, which is essential for electrical and aerosol solution engineering. The energy equations are derived from the local thermal non-equilibrium to determine the unique thermal profiles of the solid and liquid phases. The current model,…
- OpenAlex · Published · First archived
Decoupling deformation modes: a physically interpretable neural network for hyperelastic constitutive modeling ↗
Abstract Hyperelastic constitutive models traditionally rely on a single global strain energy density function defined by deformation invariants. While this approach ensures thermodynamic consistency and mathematical simplicity, it implicitly forces a uniform energy landscape across all deformation modes. As demonstrated by the classical Treloar rubber dataset, this assumption is physically restrictive: uniaxial, equibiaxial, and planar deformations exhibit distinct stiffening behaviors that a single invariant-based formulation depending only on the first invariant I 1 , such as the Gent model…
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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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