Machine learning for electrocatalyst and photocatalyst design and discovery

H Mai, TC Le, D Chen, DA Winkler… - Chemical …, 2022 - ACS Publications
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

Feature dimensionality reduction: a review

W Jia, M Sun, J Lian, S Hou - Complex & Intelligent Systems, 2022 - Springer
As basic research, it has also received increasing attention from people that the “curse of
dimensionality” will lead to increase the cost of data storage and computing; it also …

A unifying perspective on neural manifolds and circuits for cognition

C Langdon, M Genkin, TA Engel - Nature Reviews Neuroscience, 2023 - nature.com
Two different perspectives have informed efforts to explain the link between the brain and
behaviour. One approach seeks to identify neural circuit elements that carry out specific …

Reconstructing computational system dynamics from neural data with recurrent neural networks

D Durstewitz, G Koppe, MI Thurm - Nature Reviews Neuroscience, 2023 - nature.com
Computational models in neuroscience usually take the form of systems of differential
equations. The behaviour of such systems is the subject of dynamical systems theory …

[HTML][HTML] Review of artificial intelligence and machine learning technologies: Classification, restrictions, opportunities and challenges

RI Mukhamediev, Y Popova, Y Kuchin, E Zaitseva… - Mathematics, 2022 - mdpi.com
Artificial intelligence (AI) is an evolving set of technologies used for solving a wide range of
applied issues. The core of AI is machine learning (ML)—a complex of algorithms and …

Interpretable machine learning: Fundamental principles and 10 grand challenges

C Rudin, C Chen, Z Chen, H Huang… - Statistic …, 2022 - projecteuclid.org
Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

Machine learning in aerodynamic shape optimization

J Li, X Du, JRRA Martins - Progress in Aerospace Sciences, 2022 - Elsevier
Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne)

F Anowar, S Sadaoui, B Selim - Computer Science Review, 2021 - Elsevier
Abstract Feature Extraction Algorithms (FEAs) aim to address the curse of dimensionality
that makes machine learning algorithms incompetent. Our study conceptually and …

[HTML][HTML] Applications of machine learning to water resources management: A review of present status and future opportunities

AA Ahmed, S Sayed, A Abdoulhalik, S Moutari… - Journal of Cleaner …, 2024 - Elsevier
Water is the most valuable natural resource on earth that plays a critical role in the socio-
economic development of humans worldwide. Water is used for various purposes, including …

Attractor and integrator networks in the brain

M Khona, IR Fiete - Nature Reviews Neuroscience, 2022 - nature.com
In this Review, we describe the singular success of attractor neural network models in
describing how the brain maintains persistent activity states for working memory, corrects …