Results 91 to 100 of about 1,466,211 (327)

3D In Vitro Models of Breast Cancer: Current Challenges and Future Prospects Toward Recapitulating the Microenvironment and Mimicking Key Processes

Advanced Biology, EarlyView.
In vitro cancer models are advantageous for studying important processes such as tumorigenesis, cancer growth, invasion, and metastasis. The complexity and biological relevance increase depending on the model structure, organization, and composition of materials and cells.
Kyndra S. Higgins, Ah Joung Yu, Cheryl T. Gomillion   +2 more
wiley   +1 more source

Learning Transformed Dynamics for Efficient Control Purposes

Mathematics
Learning linear and nonlinear dynamical systems from available data is a timely topic in scientific machine learning. Learning must be performed while enforcing the numerical stability of the learned model, the existing knowledge within an informed or ...
Chady Ghnatios, Joel Mouterde, Jerome Tomezyk, Joaquim Da Silva, Francisco Chinesta   +4 more
doaj   +1 more source

Consolidate Overview of Ribonucleic Acid Molecular Dynamics: From Molecular Movements to Material Innovations

Advanced Engineering Materials, EarlyView.
Molecular dynamics simulations are advancing the study of ribonucleic acid (RNA) and RNA‐conjugated molecules. These developments include improvements in force fields, long‐timescale dynamics, and coarse‐grained models, addressing limitations and refining methods.
Kanchan Yadav, Iksoo Jang, Jong Bum Lee
wiley   +1 more source

The stability of macroeconomic systems with Bayesian learners [PDF]

We study abstract macroeconomic systems in which expectations play an important role. Consistent with the recent literature on recursive learning and expectations, we replace the agents in the economy with econometricians.
Jacek Suda, James B. Bullard
core  

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source

Fine-Tuning Quadcopter Control Parameters via Deep Actor-Critic Learning Framework: An Exploration of Nonlinear Stability Analysis and Intelligent Gain Tuning

IEEE Access
Quadcopters have underactuated, nonlinear, and coupled dynamics, making their control a challenging endeavor. However, PID controllers have exhibited remarkable performance for such systems in a variety of circumstances, including obstacle avoidance ...
Hassan Moin, Umer Hameed Shah, Muhammad Jawad Khan, Hasan Sajid   +3 more
doaj   +1 more source

Game Theory-Inspired Evolutionary Algorithm for Global Optimization

Algorithms, 2017
Many approaches that model specific intelligent behaviors perform excellently in solving complex optimization problems. Game theory is widely recognized as an important tool in many fields.
Guanci Yang
doaj   +1 more source

Low‐Activation Compositionally Complex Alloys for Advanced Nuclear Applications—A Review

Advanced Engineering Materials, EarlyView.
Low‐activation compositionally complex alloys (LACCAs) are advanced metallic materials primarily composed of low‐activation elements, offering advantages such as rapid compliance with operational standards and safe recyclability. This review highlights their potential for extreme high‐temperature irradiation environments as structural materials for ...
Yangfan Wang, Hanliang Zhu, Zhiyang Wang, Zhijun Qiu, Xiao Wang, Zengxi Pan, Hongtao Zhu, Mingxing Zhang, Huijun Li   +8 more
wiley   +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

Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery

Chemistry of Materials
The discovery of new crystalline inorganic compounds—novel compositions of matter within known structure types, or even compounds with completely new crystal structures—constitutes an important goal of solid-state and materials chemistry.
Anthony K. Cheetham, Ram Seshadri
semanticscholar   +1 more source