Magnetohydrodynamics with physics informed neural operators di Shawn G Rosofsky, E A Huerta

“...Machine Learning: Science and Technology...”
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Stochastic gradient descent with random label noises: doubly stochastic models and inference stabilizer di Haoyi Xiong, Xuhong Li, Boyang Yu, Dongrui Wu, Zhanxing Zhu, Dejing Dou

“...Machine Learning: Science and Technology...”
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Multi-target quantum compilation algorithm di Vu Tuan Hai, Nguyen Tan Viet, Jesus Urbaneja, Nguyen Vu Linh, Lan Nguyen Tran, Le Bin Ho

“...Machine Learning: Science and Technology...”
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Effectual pre-processing with quantization error elimination in pose detector with the aid of image-guided progressive graph convolution network (IGP-GCN) for multi-person pose est... di Jhansi Rani Challapalli, Nagaraju Devarakonda

“...Machine Learning: Science and Technology...”
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Beware of diffusion models for synthesizing medical images—a comparison with GANs in terms of memorizing brain MRI and chest x-ray images di Muhammad Usman Akbar, Wuhao Wang, Anders Eklund

“...Machine Learning: Science and Technology...”
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A study of transfer learning in digital rock properties measurement di M I K Haq, I N Yulita, I A Dharmawan

“...Machine Learning: Science and Technology...”
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Neural network field theories: non-Gaussianity, actions, and locality di Mehmet Demirtas, James Halverson, Anindita Maiti, Matthew D Schwartz, Keegan Stoner

“...Machine Learning: Science and Technology...”
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Machine learning inspired models for Hall effects in non-collinear magnets di Jonathan Kipp, Fabian R Lux, Thorben Pürling, Abigail Morrison, Stefan Blügel, Daniele Pinna, Yuriy Mokrousov

“...Machine Learning: Science and Technology...”
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Unification of symmetries inside neural networks: transformer, feedforward and neural ODE di Koji Hashimoto, Yuji Hirono, Akiyoshi Sannai

“...Machine Learning: Science and Technology...”
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Neural network Gaussian processes as efficient models of potential energy surfaces for polyatomic molecules di J Dai, R V Krems

“...Machine Learning: Science and Technology...”
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Online meta-learned gradient norms for active learning in science and technology di Haiqi Dong, Amanda S Barnard, Amanda J Parker

“...Machine Learning: Science and Technology...”
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Reducing training data needs with minimal multilevel machine learning (M3L) di Stefan Heinen, Danish Khan, Guido Falk von Rudorff, Konstantin Karandashev, Daniel Jose Arismendi Arrieta, Alastair J A Price, Surajit Nandi, Arghya Bhowmik, Kersti Hermansson, O Anatole von Lilienfeld

“...Machine Learning: Science and Technology...”
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From STEM-EDXS data to phase separation and quantification using physics-guided NMF di Adrien Teurtrie, Nathanaël Perraudin, Thomas Holvoet, Hui Chen, Duncan T L Alexander, Guillaume Obozinski, Cécile Hébert

“...Machine Learning: Science and Technology...”
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ATSFCNN: a novel attention-based triple-stream fused CNN model for hyperspectral image classification di Jizhen Cai, Clotilde Boust, Alamin Mansouri

“...Machine Learning: Science and Technology...”
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CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique di Mohammad H Alshayeji

“...Machine Learning: Science and Technology...”
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An exponentially-growing family of universal quantum circuits di Mo Kordzanganeh, Pavel Sekatski, Leonid Fedichkin, Alexey Melnikov

“...Machine Learning: Science and Technology...”
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Efficient determination of the Hamiltonian and electronic properties using graph neural network with complete local coordinates di Mao Su, Ji-Hui Yang, Hong-Jun Xiang, Xin-Gao Gong

“...Machine Learning: Science and Technology...”
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Quantum resources of quantum and classical variational methods di Thomas Spriggs, Arash Ahmadi, Bokai Chen, Eliska Greplova

“...Machine Learning: Science and Technology...”
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Deep learning representations for quantum many-body systems on heterogeneous hardware di Xiao Liang, Mingfan Li, Qian Xiao, Junshi Chen, Chao Yang, Hong An, Lixin He

“...Machine Learning: Science and Technology...”
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Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function di Jiawei Zang, Matija Medvidović, Dominik Kiese, Domenico Di Sante, Anirvan M Sengupta, Andrew J Millis

“...Machine Learning: Science and Technology...”
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