Results 151 to 160 of about 4,789,872 (297)

Machine learning potential era of zeolite simulation. [PDF]

Chem Sci, 2022
Ma S, Liu ZP.
europepmc   +1 more source

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source

Student stress and mental health during online learning: Potential for post-COVID-19 school curriculum development. [PDF]

Compr Psychoneuroendocrinol, 2023
Nuryana Z, Xu W, Kurniawan L, Sutanti N, Makruf SA, Nurcahyati I.   +5 more
europepmc   +1 more source

A Robust Adaptive One‐Sample‐Ahead Preview Super‐Twisting Sliding Mode Controller

International Journal of Adaptive Control and Signal Processing, EarlyView.
Block Diagram of the Robust Adaptive One‐Sample‐Ahead Preview Super‐Twisting Sliding Mode Controller. ABSTRACT This article introduces a discrete‐time robust adaptive one‐sample‐ahead preview super‐twisting sliding mode controller. A stability analysis of the controller by Lyapunov criteria is developed to demonstrate its robustness in handling both ...
Guilherme Vieira Hollweg, Paulo Jefferson Dias de Oliveira Evald, Shivam Chaturvedi, Hilton Abilio Gründling, Rodrigo Varella Tambara, Wencong Su   +5 more
wiley   +1 more source

Machine learning potential for modelling dynamic hydrogen bond networks in MOF MIL-120. [PDF]

Chem Sci
Jin X, Li Y, Gaedecke K, Zhang X, Smit B.   +4 more
europepmc   +1 more source

Choosing the right molecular machine learning potential. [PDF]

Chem Sci, 2021
Pinheiro M, Ge F, Ferré N, Dral PO, Barbatti M.   +4 more
europepmc   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Proton Transport on Graphamine: A Deep-Learning Potential Study. [PDF]

J Phys Chem C Nanomater Interfaces
Ananthabhotla LY, Achar SK, Johnson JK.
europepmc   +1 more source

Machine learning potential for interacting dislocations in the presence of free surfaces. [PDF]

Sci Rep, 2022
Lanzoni D, Rovaris F, Montalenti F.
europepmc   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source