AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A novel PIDL model is proposed called DoubleGAN, which encodes the stochastic physics into the PIDL structure and captures the uncertain from vehicle trajectory data and introduces a stochastic CF model to inform the generator with prior physics information.
Inspired by the highly complex and nonlinear traffic flow phenomenon, we propose a second-order continuum model for autonomous vehicles (AVs) by considering them as rational and utility-optimizing agents. Further, by accounting for stochastic fluctuations in driving dynamics, a non-cooperative differential game probl...
This paper integrates multiple UQ baseline methods with physics-informed modeling to systematically investigate how physical constraints influence uncertainty estimation in traffic forecasting and proposes a physics-informed loss function that enhances the model's ability to capture physically consistent dynamics and i...
This paper investigates whether state-of-the-art deep learning trajectory generators can be adapted to satisfy differential privacy (DP) while retaining usable utility, and introduces RouteMIA, the first closed-box membership inference attack tailored to synthetic trajectories.
Physics-Informed Neural Networks (PINNs) are a widely used data-free method for solving Partial Differential Equations (PDEs) using machine learning. With recent advances in Generative Adversarial Networks (GANs), adversarial learning has shown strong capabilities for modeling complex data-driven problems; however, the...
Robotic manipulation videos are increasingly used as visual plans for embodied agents, but optimizing purely for visual plausibility often fails to capture the fragile physical manifold of real-world interactions. Even minor physics-violating errors at the interaction boundary, such as interpenetration or premature obj...
Topology optimization, using both physic-based approaches and deep learning surrogates, serves as a cornerstone for generative design agents in cyber-manufacturing systems. While deep learning surrogates have gained widespread adoption due to their speed in online design generation, this work demonstrates their vulnera...
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