Results 91 to 100 of about 2,419,332 (252)

Deterministic Transmission Scheduling Mechanism for Mixed Traffic Flows Towards Digital Twin Networks [PDF]

Jisuanji kexue
A deterministic transmission scheduling mechanism based on deep reinforcement learning under the digital twin architecture is proposed for the end-to-end transmission of mixed traffic flows in railway operation and maintenance scenarios,namely end-to-end
WANG Kewen, ZHANG Weiting, LIAO Peixi
doaj   +1 more source

Data‐Driven and Physics‐Informed Modeling Perspectives for Soft Manipulators: From SINDy to Hybrid Intelligence

Advanced Science, EarlyView.
This Perspective charts the modeling landscape for soft manipulators, spanning sparse system identification, neural dynamics, physics‐informed models and hybrid intelligence. We examine how physics, data, sensing, adaptation and control can assume complementary roles within this landscape, and provide a framework for selecting modeling approaches ...
Wenbo Hui, Yunyi Wang, Teng Zhang, Yang Chen, Min Pan   +4 more
wiley   +1 more source

From Artificial to Embodied and Physical Intelligence in Smart Manufacturing

Advanced Science, EarlyView.
Smart manufacturing is reframed through complementary artificial, embodied, and physical intelligence, spanning computational reasoning, perception‐action coupling, and material‐level adaptation. Their integration across design, fabrication, operation, and maintenance reveals how intelligence can be co‐designed across algorithms, embodied agents, and ...
Kang Wang, Xinyu Lu, Tong Liu, Jinjie Fang, Xin Li, Siqian Wu, Liuchao Jin, Wei‐Hsin Liao, Shuyou Zhang, Jianrong Tan   +9 more
wiley   +1 more source

Device Stochasticity Directly Implements Bayesian Sampling in Spiking Neurons

Advanced Science, EarlyView.
Brain‐inspired stochastic spiking turns device noise into Bayesian uncertainty for robust, low‐power neuromorphic computing. ABSTRACT Device stochasticity is considered a non‐ideality in neuromorphic hardware, although stochastic neural activity supports probabilistic computation in biological systems.
Huannan Zheng, Jingli Liu, Kezhou Yang
wiley   +1 more source

Uncertainty-aware structure–property mapping of spinodoid metamaterials

Materials & Design
Spinodoid metamaterials provide a tunable design space for anisotropic mechanical properties, but their structure–property relationships are commonly represented as deterministic mappings from cone-angle descriptors to single stiffness values.
Minwoo Park, Junseo Park, Mingyu Lee, Hugon Lee, Hanbin Cho, Ikjin Lee, Seunghwa Ryu   +6 more
doaj   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Risk averse stochastic structural topology optimization

, 2018
A novel approach for risk-averse structural topology optimization under uncertainties is presented, which takes into account stochastic data of the state equation, specifically random material properties and random forces.
Reinhold Schneider, Sebastian Wolf, Johannes Neumann, Schneider, Reinhold, Martin Eigel, Neumann, Johannes, Eigel, Martin, Wolf, Sebastian   +7 more
core   +1 more source

Stochastic‐MTJ Sampler Arrays for In‐Array Monte–Carlo Estimation

Advanced Electronic Materials, EarlyView.
A low‐energy‐barrier magnetic tunnel junction array is operated as a probability‐domain sampler: each cell's random switching, programmed through a shared digital‐to‐analog converter, makes the per‐column multiply–accumulate an unbiased Monte–Carlo expectation estimator that returns both a mean and a calibrated uncertainty.
Ran Zhang, Nellie Laleni, Sina Ranjbar, Andreas Tsiougkos, Nandakishor Yadav, Vasilis Pavlidis, Thomas Kämpfe   +6 more
wiley   +1 more source

Long-Term Sum-Rate Maximization in Irregular RIS-Aided V2X Communications Using Lyapunov Optimization

IEEE Open Journal of the Communications Society
In emerging smart cities and intelligent transportation systems (ITS) ecosystems, sixth-generation (6G) vehicular networks demand sustainable wireless environments capable of adapting to high mobility and dynamic propagation conditions.
Lavanya Ganeshkumar, Ramanathan Ramachandran, Tomoaki Ohtsuki   +2 more
doaj   +1 more source

Uncertainty Aware Structural Topology Optimization Via a Stochastic Reduced Order Model Approach [PDF]

, 2017
This work presents a stochastic reduced order modeling strategy for the quantification and propagation of uncertainties in topology optimization. Uncertainty aware optimization problems can be computationally complex due to the substantial number of ...
Warner, James E., Aguilo, Miguel A.
core