Results 121 to 130 of about 60,012 (291)
AI in chemical engineering: From promise to practice
AIChE Journal, EarlyView.Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
AIChE Journal, EarlyView.Abstract Electrification of distillation offers a promising route to reducing scope‐1 emissions from one of the chemical industry's most energy‐intensive unit operations. However, conventional adiabatic columns are dynamically inflexible: Long, energy‐intensive start‐ups make shutdown and restart impractical under variable electricity prices and ...
Samuel Mercer, Michael Baldea
wiley +1 more source
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Advanced Intelligent Discovery, EarlyView.Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source
Linear Active Disturbance Rejection Control for LCL Type Grid-connected Converter [PDF]
, 2016 Guerrero, Josep M. +4 more
core +1 more source
Self-tuning method for a linear active disturbance rejection controller
工程科学学报, 2015 Summary: This paper introduces a parameter self-tuning algorithm based on dynamic response time series data mining to solve the parameter self-tuning difficulty of a Linear Active Disturbance Rejection Controller (LADRC). In the algorithm, two gain signal loops' dynamic response of Linear State Error Feedback (LSEF) is used as the parameter adjustment ...
LI Yang, WANG Jing, ZHANG Yong-jun
openaire +2 more sources
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
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Control and structural optimization for maneuvering large spacecraft [PDF]
Presented here are the results of an advanced control design as well as a discussion of the requirements for automating both the structures and control design efforts for maneuvering a large spacecraft.
Chun, H. M., Turner, J. D., Yu, C. C.
core +1 more source
Advanced Intelligent Discovery, EarlyView.scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta +3 more
wiley +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker +2 more
wiley +1 more source