Results 41 to 50 of about 82,970 (153)

Exploration and Exploitation of New Knowledge Emergence to Improve the Collective Intelligent Decision-Making Level of Web-of-Cells With Cyber-Physical-Social Systems Based on Complex Network Modeling

IEEE Access, 2018
Through exploration and exploitation of new knowledge emergence, the collective intelligent decision-making (CID) level of Web-of-Cells (WoC) proposed by ELECTRA will be dramatically improved.
Lefeng Cheng, Tao Yu
doaj   +1 more source

SMC-Based, Stability-Guided Deep Reinforcement Learning for Control of Uncertain Nonlinear Dynamic Systems

IEEE Access
This paper presents a novel control framework that integrates the stability insights of traditional control with reinforcement learning (RL) to tackle uncertain nonlinear dynamic systems.
Mahya Ramezani, M. Amin Alandihallaj, Andreas M. Hein, Holger Voos   +3 more
doaj   +1 more source

Integral Signatures of Activation Functions: A 9-Dimensional Taxonomy and Stability Theory for Deep Learning

CoRR
25 ...
Ankur Mali, Lawrence Hall, Jake Williams, Gordon Richards   +3 more
openaire   +2 more sources

Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations

Machine Learning: Science and Technology
Active learning (AL) requires massive time for comprehensive sampling of complex potential energy surfaces to achieve desirable accuracy and stability of machine learning (ML) potentials.
Yaohuang Huang, Yi-Fan Hou, Pavlo O Dral
doaj   +1 more source

Optimal Control Based on Reinforcement Learning for Flexible High-Rise Buildings with Time-Varying Actuator Failures and Asymmetric State Constraints

Buildings
This study centers on the vibration suppression of high-rise building systems under extreme conditions, exploring a reinforcement learning (RL)-based vibration control strategy for flexible building systems with time-varying faults and asymmetric state ...
Min Li, Rui Xie
doaj   +1 more source

Decoding the Stability of Transition-Metal Alloys with Theory-infused Deep Learning

We introduce an interpretable deep learning framework that predicts the cohesive energy of transition-metal alloys (TMAs) by embedding cohesion theory within graph neural networks (GNNs). Beyond accurate prediction of cohesive energy, a key indicator of thermodynamic stability, the model offers mechanistic insights by disentangling energy contributions
Huang, Yang, Wang, Shih-Han, Cao, Shuyi, Achenie, Luke E. K., Xin, Hongliang   +4 more
openaire   +2 more sources

Accurate screening of functional materials with machine-learning potential and transfer-learned regressions: Heusler alloy benchmark

npj Computational Materials
We present a machine learning-accelerated high-throughput (HTP) workflow for the discovery of functional materials. As a test case, quaternary and all-d Heusler compounds were screened for stable compounds with large magnetocrystalline anisotropy energy (
Enda Xiao, Terumasa Tadano
doaj   +1 more source

Method of Motion Planning for Digital Twin Navigation and Cutting of Shearer

Sensors
To further enhance the intelligence level of coal mining faces and achieve the autonomous derivation, learning, and optimization of shearer navigation cutting, this paper proposes the methods of shearer digital twin navigation cutting motion planning ...
Bing Miao, Shirong Ge, Yunwang Li, Yinan Guo   +3 more
doaj   +1 more source

Unified Theory of Machine Learning : A Dynamical Theory of Stability, Generalization, and Solvability

An independent reseacher from ...
openaire   +2 more sources

On the Importance of Learning Non‐Local Dynamics for Stable Data‐Driven Climate Modeling: A 1D Gravity Wave‐QBO Testbed

Geophysical Research Letters
Model instability remains a core challenge for data‐driven parameterizations, especially those developed with supervised algorithms, and rigorous methods to address it are lacking.
Hamid A. Pahlavan, Pedram Hassanzadeh, M. Joan Alexander   +2 more
doaj   +1 more source