This paper introduces a novel dynamic topology optimization algorithm based on graph neural networks (GNNs). Traditional topology optimization methods are often static and require manual parameter tuning. Our algorithm automatically adjusts the topology structure based on the system's inherent properties, offering significant improvements in efficiency and robustness. We propose a framework that leverages graph representations to capture the system's topology, enabling a more adaptable and efficient optimization process. This work addresses a critical limitation of existing methods by providing a dynamic and intelligent approach to topology design. The core mechanism centers around utilizing GNNs to represent and manipulate the topology, resulting in optimized solutions with enhanced adaptability. We present a comprehensive analysis of the algorithm's performance, including experimental results demonstrating its effectiveness across various topology configurations.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel dynamic quantum algorithm leveraging self-adaptive graph neural networks (S-GNNs). Traditional quantum algorithms often rely on static parameter settings, limiting their adaptability to complex input data. Our approach introduces a dynamically adjusting S-GNN that automatically adjusts the connection weights and neuron counts, enabling more efficient quantum computation. We demonstrate the algorithm's effectiveness through a series of benchmark tests, highlighting its ability to navigate intricate quantum circuits and achieve improved performance compared to existing methods. The core mechanism centers on a feedback loop that continuously refines the network based on the input, optimizing for a specific quantum circuit's properties. This contributes to a significant advancement in quantum algorithm design and optimization.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The development of quantum information processing has spurred significant research into quantum topology, offering the potential for novel quantum algorithms and devices. However, designing and optimizing quantum circuits with complex topologies remains a challenging task. This paper proposes a novel algorithm, "基于图的自适应量子拓扑优化", that leverages graph neural networks (GNNs) to dynamically adjust quantum topology during optimization. We demonstrate the effectiveness of this approach through the optimization of a specific quantum circuit, showcasing improved convergence and efficiency compared to traditional methods. This work introduces a framework that intelligently refines quantum states based on the underlying topology, addressing a key limitation of existing approaches.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to program code semantic understanding and reconstruction leveraging Graph Neural Networks (GNNs). The core idea is to directly learn semantic relationships within program code by representing it as a graph structure. Nodes in the graph represent code elements such as functions, variables, and statements, while edges represent the semantic relationships between them, such as call relationships and dependencies. The GNN learns node and edge features from this graph structure, enabling automated code understanding, reconstruction, and optimization. Traditional methods relying on manual analysis and rule-based engines suffer from low efficiency and potential errors. This work demonstrates the effectiveness of GNNs in automating these tasks, offering a more scalable and robust solution for code manipulation. The presented framework aims to improve code maintainability, reduce development time, and facilitate code optimization through a deeper understanding of code semantics. The key contribution lies in the application of GNNs to the specific domain of program code, moving beyond traditional symbolic representations and enabling a more nuanced and effective approach to code analysis and transformation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel graph neural network (GNN) architecture, termed "Adaptive Graph Neural Network" (AGNN), designed to dynamically adjust its graph structure in response to data fluctuations. The AGNN's core mechanism centers around a dynamic parameterization of the graph, enabling efficient knowledge transfer and generalization. We demonstrate the AGNN's effectiveness through a series of experiments on a diverse dataset, showcasing its superior performance compared to existing GNN models in scenarios requiring adaptive learning. The paper highlights the benefits of this approach, including improved robustness and generalization capabilities.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of Graph Neural Networks (GNNs) to dynamic path planning, offering a novel approach to address the limitations of traditional methods. Dynamic path planning necessitates the ability to adapt to changing environmental conditions and real-time data, which is often challenging with static maps. This work proposes a GNN-based algorithm that dynamically updates path strategies based on node and edge changes, leading to more intelligent and robust path generation. We explore the benefits of this approach in scenarios involving unpredictable environments and varying demand. The core mechanism leverages GNNs to predict future path trajectories, enabling continuous adaptation and optimization. This research demonstrates the potential of GNNs to revolutionize dynamic path planning, offering improved efficiency and resilience.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to geometric modeling that leverages graph neural networks (GNNs) for adaptive complexity learning. Traditional methods often require manual design of complex geometric models, leading to high modeling costs. We propose a system that dynamically adjusts the complexity of GNNs based on the model's computational cost, enabling automatic adaptation and improved model performance. This research explores the use of GNNs to represent and learn complex geometric structures, demonstrating a method for automated complexity control and reduced modeling effort. The core mechanism centers around utilizing GNNs to represent the geometric structure and adjust network parameters based on the model's computational demands. This results in a system capable of generating and maintaining highly complex geometric models with minimal manual intervention.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel system of framework for predicting system states and enabling collaborative control utilizing graph neural networks (GNNs). The core idea is to leverage graph structure to model system dynamics, identify key nodes and their relationships, and facilitate efficient control strategies. We propose a GNN architecture that learns node representations based on the graph topology, enabling the model to effectively capture dependencies and predict future states. The proposed system demonstrates improved performance compared to traditional methods in predicting system behavior, especially in scenarios with complex dynamics. Furthermore, we showcase the potential for enhanced control through coordinated actions based on the identified graph structure. The research focuses on achieving optimal performance through careful design of the GNN architecture and the selection of appropriate node representation learning techniques. This work contributes to the advancement of intelligent systems by providing a robust and adaptable framework for state prediction and control.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel framework for complex system modeling based on graph neural networks (GNNs). We propose a method to automatically learn complex system structures from data, facilitating efficient complexity analysis. Traditional approaches often require manual model design, while GNNs offer a powerful mechanism for learning intricate structural representations. The proposed framework leverages GNNs to represent the system as a graph, where nodes represent elements and edges represent relationships. The model learns node embeddings that capture the system's overall structure, enabling efficient complexity analysis. We demonstrate the effectiveness of this approach through several case studies, highlighting its ability to capture intricate patterns and provide insightful complexity assessments. This work represents a significant advancement in complex system modeling, offering a more automated and flexible approach compared to existing methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to neural-symbolic reasoning by introducing a Dynamic Topological Memory Network (DTMN). The core idea is to construct a memory network capable of dynamically adjusting its internal topology to mirror the complexity and relationships within the input data. This addresses a key limitation of existing neural-symbolic methods that often rely on static knowledge graphs or predefined rules, struggling with uncertainty and intricate relationships. The DTMN incorporates a graph-based neural architecture where nodes represent concepts or facts, and edges represent their relationships. A "topological learner" dynamically adjusts the network's structure—including adding, removing, or modifying nodes and edges—using techniques like reinforcement learning or evolutionary algorithms, guided by the input data and existing knowledge. A "symbolic reasoning engine" then leverages this evolving topology for logical inference, generating symbolic expressions as output. The significance lies in the adaptive nature of the network's topology, enabling a more flexible and effective reasoning process compared to traditional methods. We demonstrate the potential of this architecture for robust and efficient symbolic reasoning tasks.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the optimization of distributed resource scheduling using a multi-agent reinforcement learning (MARL) framework. Traditional resource scheduling methods often rely on static rules or centralized control, which can be inflexible and inefficient in dynamic environments. This research proposes a novel approach where resource scheduling is formulated as a multi-agent reinforcement learning problem. Each agent is responsible for allocating a portion of resources, and they learn collaboratively through reinforcement learning to achieve global optimality. The system adapts dynamically to changing conditions, offering a more efficient and robust solution compared to traditional methods. The key contributions of this work lie in the application of MARL to resource scheduling, demonstrating its potential for improved performance and adaptability. The model is defined as (St, At, Rt, Pt), where St is the state, At is the action, Rt is the reward, and Pt is the transition probability.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of Deep Reinforcement Learning (DRL) for intelligent traffic signal control. Traditional traffic signal control methods often rely on pre-defined rules or simple optimization algorithms, which may not effectively adapt to dynamic traffic conditions. DRL offers a promising approach by allowing an agent to learn optimal control policies through trial and error interactions with the traffic environment. This research proposes a DRL framework for dynamic traffic signal control, aiming to mitigate congestion and improve traffic flow. The framework utilizes a deep neural network to approximate the Q-function, enabling the agent to learn complex traffic patterns and adapt its control strategy accordingly. The effectiveness of the proposed approach is evaluated through simulations, demonstrating its potential to outperform conventional control methods. The core claim of this work is to leverage DRL algorithms to optimize traffic flow and reduce congestion. The central mechanism involves modeling traffic signal control as a DRL problem and utilizing a deep neural network to learn optimal control policies. This represents a novel approach to traffic management, aiming to enhance optimization efficiency.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations