Results 51 to 60 of about 522,455 (311)

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

IEEE Access, 2022
Networked control systems have gained considerable attention over the last decade as a result of the trend towards decentralised control applications and the emergence of cyber-physical system applications.
Leila Sedghi, Zohaib Ijaz, Md. Noor-A-Rahim, Kritchai Witheephanich, Dirk Pesch   +4 more
doaj   +1 more source

The human gut microbiome across the life course

FEBS Letters, EarlyView.
Despite significant individual variation and continuous change throughout life, the human gut microbiome follows some life stage‐specific trends. This article provides a brief overview of how gut microbiome composition shifts across different phases of life. Created in BioRender. Özkurt, E. (2026) https://BioRender.com/8q4nrnc.
Alise J. Ponsero, Basak Bahcivanci, Antonietta Hayhoe, Animesh Acharjee, Ezgi Özkurt   +4 more
wiley   +1 more source

Groundwater Prediction Using Machine-Learning Tools

, 2020
Predicting groundwater availability is important to water sustainability and drought mitigation. Machine-learning tools have the potential to improve groundwater prediction, thus enabling resource planners to: (1) anticipate water quality in unsampled ...
Christopher Thron, Mattia Vaccari, Eslam A. Hussein, Antoine Bagula, Mehrdad Ghaziasgar   +4 more
core   +1 more source

Data Complexity in Machine Learning and Novel Classification Algorithms [PDF]

, 2006
This thesis summarizes four of my research projects in machine learning. One of them is on a theoretical challenge of defining and exploring complexity measures for data sets; the others are about new and improved classification algorithms.
Li, Ling
core   +1 more source

Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges [PDF]

, 2022
In a digitalized era and with the rapid growth of computational skills and advancements, artificial intelligence and Machine Learning uses in various applications are gaining a rising interest from scholars and practitioners.
Christos Ioakimidis,, Sesil Koutra, Christos S. Ioakimidis, Koutra, Sesil   +3 more
core   +1 more source

Large‐Scale Atomistic Simulations of Magnesium Oxide Exsolution Driven by Machine Learning Potentials: Implications for the Early Geodynamo

Geophysical Research Letters
The precipitation of magnesium oxide (MgO) from the Earth's core has been proposed as a potential energy source to power the geodynamo prior to the inner core solidification. Yet, the stable phase and exact amount of MgO exsolution remain elusive.
Jie Deng
doaj   +1 more source

Modelling stem cell differentiation related processes—A practical overview for biologists

FEBS Letters, EarlyView.
Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar, Xinqi Yan, Lucas Jansen Klomp, Stefano Schivo, Janine N. Post   +4 more
wiley   +1 more source

Machine learning methods in chemoinformatics

, 2014
Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion.
John B. O. Mitchell, Mitchell, J.B.O.
core   +1 more source

PAL – parallel active learning for machine-learned potentials

Digital Discovery
An automated, modular, and parallel active learning (PAL) library that integrates AL tasks and manages their execution and communication on shared- and distributed-memory systems using the Message Passing Interface (MPI).
Chen Zhou, Marlen Neubert, Yuri Koide, Yumeng Zhang, Van-Quan Vuong, Tobias Schlöder, Stefanie Dehnen, Pascal Friederich   +7 more
openaire   +4 more sources

A Framework for a Generalization Analysis of Machine-Learned Interatomic Potentials

Multiscale Modeling & Simulation, 2023
Machine-learned interatomic potentials (MLIPs) and force fields (i.e. interaction laws for atoms and molecules) are typically trained on limited data-sets that cover only a very small section of the full space of possible input structures. MLIPs are nevertheless capable of making accurate predictions of forces and energies in simulations involving ...
Christoph Ortner, Yangshuai Wang
openaire   +2 more sources