Search for: All records

Large deployable mesh reflectors play a critical role in satellite communications, Earth observation, and deep-space exploration, offering high-gain antenna performance through precisely shaped reflective surfaces. Traditional dynamic modeling approaches—such as wave-based and finite element methods—often struggle to accurately capture the complex behavior of three-dimensional reflectors due to oversimplifications of cable members. To address these challenges, this paper proposes a novel spatial discretization framework that systematically decomposes cable member displacements into boundary-induced and internal components in a global Cartesian coordinate system. The framework derives a system of ordinary differential equations for each cable member by enforcing the Lagrange’s equations, capturing both longitudinal and transverse internal displacement of the cable member. Numerical simulations of a two-dimensional cable-network structure and a center-feed parabolic deployable mesh reflector with 101 nodes illustrate the improved accuracy of the proposed method in predicting vibration characteristics across a broad frequency range. Compared to standard finite element analysis, the proposed method more effectively identifies both low- and high-frequency modes and offers robust convergence and accurate prediction for both frequency and transient responses of the structure. This enhanced predictive capability underscores the significance of incorporating internal cable member displacements for reliable dynamic modeling of large deployable mesh reflectors, ultimately informing better design, control, and on-orbit performance of future space-based reflector systems. 

Abstract Energy efficiency in computation is ultimately limited by noise, with quantum limits setting the fundamental noise floor. Analog physical neural networks hold promise for improved energy efficiency compared to digital electronic neural networks. However, they are typically operated in a relatively high-power regime so that the signal-to-noise ratio (SNR) is large (>10), and the noise can be treated as a perturbation. We study optical neural networks where all layers except the last are operated in the limit that each neuron can be activated by just a single photon, and as a result the noise on neuron activations is no longer merely perturbative. We show that by using a physics-based probabilistic model of the neuron activations in training, it is possible to perform accurate machine-learning inference in spite of the extremely high shot noise (SNR  ~ 1). We experimentally demonstrated MNIST handwritten-digit classification with a test accuracy of 98% using an optical neural network with a hidden layer operating in the single-photon regime; the optical energy used to perform the classification corresponds to just 0.038 photons per multiply-accumulate (MAC) operation. Our physics-aware stochastic training approach might also prove useful with non-optical ultra-low-power hardware. 

Abstract AstroSage-Llama-3.1-8B is a domain-specialized natural-language AI assistant tailored for research in astronomy, astrophysics, cosmology, and astronomical instrumentation. Trained on the complete collection of astronomy-related arXiv papers from 2007 to 2024 along with millions of synthetically-generated question-answer pairs and other astronomical literature, AstroSage-Llama-3.1-8B demonstrates remarkable proficiency on a wide range of questions. AstroSage-Llama-3.1-8B scores 80.9% on the AstroMLab-1 benchmark, greatly outperforming all models—proprietary and open-weight—in the 8-billion parameter class, and performing on par with GPT-4o. This achievement demonstrates the potential of domain specialization in AI, suggesting that focused training can yield capabilities exceeding those of much larger, general-purpose models. AstroSage-Llama-3.1-8B is freely available, enabling widespread access to advanced AI capabilities for astronomical education and research.