Results 181 to 190 of about 93,202 (286)
Advanced Intelligent Discovery, EarlyView.This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee +3 more
wiley +1 more source
Low-Latency Oriented Joint Data Compression and Resource Allocation in NOMA-MEC Networks: A Deep Reinforcement Learning Approach. [PDF]
Sensors (Basel)Tan F, Zeng Y, Lan C, Zhou Z.
europepmc +1 more source
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator [PDF]
, 2019 Karl Krauth, Stephen Tu, Benjamin Recht
openalex +1 more source
Advanced Intelligent Discovery, EarlyView.Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
DT-aided resource allocation via generative adversarial imitation learning in complex cloud-edge-end scenarios. [PDF]
Sci RepZhang X, Xin M, Li Y, Fu Q.
europepmc +1 more source
Convergence of Online and Approximate Multiple-Step Lookahead Policy Iteration
, 2018 Yonathan Efroni +3 more
openalex +1 more source
Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning
Advanced Intelligent Discovery, EarlyView.Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang +4 more
wiley +1 more source
Federated Learning Semantic Communication in UAV Systems: PPO-Based Joint Trajectory and Resource Allocation Optimization. [PDF]
Sensors (Basel)Du S, Zhang Y, Tao Z, Li H, Mei H.
europepmc +1 more source