Results 121 to 130 of about 2,720,618 (349)

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

KPP code for MITgcm v62r with added parameterization for Langmuir Circulation

, 2020
Cristina Schultz
openalex   +2 more sources

Characterizing quantum codes via the coefficients in Knill-Laflamme conditions

npj Quantum Information
Quantum error correction (QEC) is essential for protecting quantum information against noise, yet understanding the structure of the Knill-Laflamme (KL) coefficients $$\left\{{\lambda }_{ij}\right\}$$ λ i j from the condition $$P{E}_{i}^{\dagger }{E}_{j ...
Mengxin Du, Chao Zhang, Yiu Tung Poon, Bei Zeng   +3 more
doaj   +1 more source

EmbodiedCoder: Parameterized Embodied Mobile Manipulation via Modern Coding Model

Demo Page: https://embodiedcoder.github.io/EmbodiedCoder/
Lin, Zefu, Cui, Rongxu, Hanning, Chen, Wang, Xiangyu, Xu, Junjia, Jin, Xiaojuan, Wenbo, Chen, Zhou, Hui, Fan, Lue, Li, Wenling, Zhang, Zhaoxiang   +10 more
openaire   +2 more sources

Code and extensive data for training neural networks for radiation, used in "Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0""

, 2022
Peter Ukkonen
openalex   +1 more source

ChatCFD: A Large Language Model‐Driven Agent for End‐to‐End Computational Fluid Dynamics Automation with Structured Knowledge and Reasoning

Advanced Intelligent Discovery, EarlyView.
Chat computational fluid dynamics (CFD) introduces an large language model (LLM)‐driven agent that automates OpenFOAM simulations end‐to‐end, attaining 82.1% execution success and 68.12% physical fidelity across 315 benchmarks—far surpassing prior systems.
E Fan, Kang Hu, Zhuowen Wu, Jiangyang Ge, Jiawei Miao, Yuzhi Zhang, He Sun, Weizong Wang, Tianhan Zhang   +8 more
wiley   +1 more source

On Parameterized Gallager's First Bounds for Binary Linear Codes over AWGN Channels

, 2012
In this paper, nested Gallager regions with a single parameter is introduced to exploit Gallager's first bounding technique (GFBT). We present a necessary and sufficient condition on the optimal parameter. We also present a sufficient condition (with a simple geometrical explanation) under which the optimal parameter does not depend on the signal-to ...
Ma, Xiao, Liu, Jia, Bai, Baoming
openaire   +2 more sources

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Extensive data for training neural networks for radiation. Code and data used in: "Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System"

, 2022
Peter Ukkonen
openalex   +1 more source

Multiobjective Environmental Cleanup with Autonomous Surface Vehicle Fleets Using Multitask Multiagent Deep Reinforcement Learning

Advanced Intelligent Systems, EarlyView.
This study presents a multitask strategy for plastic cleanup with autonomous surface vehicles, combining exploration and cleaning phases. A two‐headed Deep Q‐Network shared by all agents is traineded via multiobjective reinforcement learning, producing a Pareto front of trade‐offs.
Dame Seck, Samuel Yanes, Manuel Perales, Daniel Gutiérrez, Sergio Toral   +4 more
wiley   +1 more source