Learning curve extrapolation predicts neural network performance from early training epochs and has been applied to accelerate AutoML, facilitating hyperparameter tuning and neural architecture search. However, existing methods typically model the evolution of learning curves in isolation, neglecting the impact of neural network (NN) architectures, which influence the loss landscape and learning trajectories. In this work, we explore whether incorporating neural network architecture improves learning curve modeling and how to effectively integrate this architectural information. Motivated by the dynamical system view of optimization, we propose a novel architecture-aware neural differential equation model to forecast learning curves continuously. We empirically demonstrate its ability to capture the general trend of fluctuating learning curves while quantifying uncertainty through variational parameters. Our model outperforms current state-of-the-art learning curve extrapolation methods and pure time-series modeling approaches for both MLP and CNN-based learning curves. Additionally, we explore the applicability of our method in Neural Architecture Search scenarios, such as training configuration ranking.

In future space-ground integrated networks, a satellite-based core network can reduce frequent signaling interactions between satellites and ground stations, thereby enhancing network architecture and supporting global communications. Users can achieve end-to-end communication through the satellitebased User Plane Function (UPF). However, the high dynamics of Low Earth Orbit (LEO) satellites result in frequent inter-satellite handovers, significantly affecting user service continuity. Existing satellite handover strategies are overly simplistic and fail to ensure the Quality of Service (QoS). Additionally, ground users compete for satellite links based on limited observations, leading to network congestion. This paper proposes a loadbalanced, distributed, multi-agent deep reinforcement learning method for satellite handover. We formulate a combinatorial optimization problem to maximize the total utility of user-satellite associations across various service types. Each user acts based on local information and engages in distributed matching with satellites. Simulation results indicate that our method ensures QoS for various service types, optimizes load balancing, and outperforms basic handover strategies in terms of handover success rate and frequency.