Latent space dynamics learning for stiff collisional-radiative models by Xuping Xie, Qi Tang, Xianzhu Tang

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Are queries and keys always relevant? A case study on transformer wave functions by Riccardo Rende, Luciano Loris Viteritti

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Multi-exit Kolmogorov–Arnold networks: enhancing accuracy and parsimony by James Bagrow, Josh Bongard

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Forecasting high-impact research topics via machine learning on evolving knowledge graphs by Xuemei Gu, Mario Krenn

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Paired autoencoders for likelihood-free estimation in inverse problems by Matthias Chung, Emma Hart, Julianne Chung, Bas Peters, Eldad Haber

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A prediction rigidity formalism for low-cost uncertainties in trained neural networks by Filippo Bigi, Sanggyu Chong, Michele Ceriotti, Federico Grasselli

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Illuminating new and known relations between knot invariants by Jessica Craven, Mark Hughes, Vishnu Jejjala, Arjun Kar

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Atomic orbits in molecules and materials for improving machine learning force fields by Anton Charkin-Gorbulin, Artem Kokorin, Huziel E Sauceda, Stefan Chmiela, Claudio Quarti, David Beljonne, Alexandre Tkatchenko, Igor Poltavsky

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Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations by Yaohuang Huang, Yi-Fan Hou, Pavlo O Dral

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Optimal equivariant architectures from the symmetries of matrix-element likelihoods by Daniel Maître, Vishal S Ngairangbam, Michael Spannowsky

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Shuffle window transformer DeepLabV3+: a lightweight convolutional neural network and transformer based hybrid semantic segmentation network by Yane Li, Zhichao Chen, Hongxia Qi, Ming Fan, Lihua Li

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Transfer learning nonlinear plasma dynamic transitions in low dimensional embeddings via deep neural networks by Zhe Bai, Xishuo Wei, William Tang, Leonid Oliker, Zhihong Lin, Samuel Williams

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Feature extraction and classification of digital rock images via pre-trained convolutional neural network and unsupervised machine learning by Masashige Shiga, Masao Sorai, Tetsuya Morishita, Masaatsu Aichi, Naoki Nishiyama, Takashi Fujii

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys by Fei Shuang, Zixiong Wei, Kai Liu, Wei Gao, Poulumi Dey

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Generative adversarial networks for creating realistic training data for machine learning-based segmentation of FIB tomography data by Trushal Sardhara, Christian J Cyron, Martin Ritter, Roland C Aydin

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A kinetic-based regularization method for data science applications by Abhisek Ganguly, Alessandro Gabbana, Vybhav Rao, Sauro Succi, Santosh Ansumali

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Multifidelity Kolmogorov–Arnold networks by Amanda A Howard, Bruno Jacob, Panos Stinis

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A generative modeling approach to reconstructing 21 cm tomographic data by Nashwan Sabti, Ram Purandhar Reddy Sudha, Julian B Muñoz, Siddharth Mishra-Sharma, Taewook Youn

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Rapid likelihood free inference of compact binary coalescences using accelerated hardware by D Chatterjee, E Marx, W Benoit, R Kumar, M Desai, E Govorkova, A Gunny, E Moreno, R Omer, R Raikman, M Saleem, S Aggarwal, M W Coughlin, P Harris, E Katsavounidis

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Diverse explanations from data-driven and domain-driven perspectives in the physical sciences by Sichao Li, Xin Wang, Amanda Barnard

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