graph neural networks

OpenFOAM data of the lid-driven cavity, natural-convection cavity, heated cylinder wake and data center benchmarks

OpenFOAM data of the benchmarks of the manuscript "Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields".

Breaking GNN-Based NIDS via Hard-Label Black-Box Flow Injection Attacks

Beyond Heuristics: A Research Journal on AI-Driven Logic Gate Synthesis and PPA Optimization in Modern Electronic Design Automation

Beyond Heuristics is a 3-page research journal on how Artificial Intelligence is changing logic gate synthesis in Electronic Design Automation (EDA). Modern chips contain billions of gates, and traditional rule-based synthesis struggles to balance power, performance, and area. The journal reviews three AI approaches to...

A Concise Framework for AI-Driven Blockchain: Integrating DRL Consensus and GNN Security Auditing

This research introduces a novel, high-performance hybrid framework merging Deep Reinforcement Learning (DRL) for dynamic consensus optimization with Graph Neural Networks (GNN) for advanced smart contract security auditing. Traditional blockchain architectures frequently struggle with balancing scalability and securit...

Diffusion model-driven multi-objective collaborative optimization for building energy management using graph neural networks

A generative framework driven by conditional diffusion models integrated with graph neural networks integrated with graph neural networks is proposed to solve the high-dimensional nonlinear multi-objective energy optimization in building clusters.

Learning to Explain Solutions of Optimal Control Problems

The results show that the GNN model can accurately predict the optimal values of the manipulated variables, and application of explainable AI algorithms reveals equality and inequality constraints that are the most important for predicting the optimal solution.

Meal-Induced Proton Density Fat Fraction and T 2 ∗ Decrease in Supraclavicular Adipose Tissue.

Brown adipose tissue (BAT) is a metabolically active tissue in humans, located primarily within the supraclavicular adipose tissue (scAT), that can be activated by cold or high-caloric meal consumption. While the changes of proton density fat fraction (PDFF) upon cold activation are well investigated, there is a knowle...

FOSLS-deRhaNN: native de Rham neural classes for H(div) and H(curl) with applications to first-order system least-squares neural network methods for partial differential equations

We construct neural approximation classes native to the graph spaces H(div) and H(curl), in two and three dimensions and, for H(div), in any dimension. Every realization lies in the space for all parameter values, and with kinked potentials, such as ReLU networks, the admissible jumps appear at finite width. The classe...

Graph Neural Network-Driven Deep Reinforcement Learning for Scalable RIS Allocation

A scalable framework combining Graph Neural Networks (GNNs) with Deep Reinforcement Learning (DRL) for dynamic shared RIS orchestration and introduces a physical topology sparsification strategy that prunes dense channel matrices into a sparse tripartite graph, improving global coverage probability while reducing compu...

Interpretable Multimodal Engagement Prediction with Graph-based Mixture-of-Experts

A graph-based Mixture-of-Experts (MoE) framework that explicitly separates self and social influences to enable interpretable engagement modeling, which outperforms baselines by up to 64.1% while offering interpretable insights into engagement dynamics across language and gender groups.

Attention-enhanced graph neural networks for nonlinearity compensation in optical fiber communication

This paper presents AEGNN (attention-enhanced graph neural network), framework that integrated Graph Attention Networks (GAT) with multi-head self-attention mechanisms to model space-time topology of optical networks and is the first fully reproducible framework combining attention-based GNNs with open-source optical c...

Causal Graph Constrained LLM for Fault Diagnosis in Industrial Data Centers

Industrial data-center systems contain complex component dependencies and long fault-propagation chains, making accurate root-cause localization difficult. Large language models (LLMs) provide a promising solution because of their strong ability to understand, organize, and reason over heterogeneous operational evidenc...