Machine Learning Science and Technology最新文献_第5页

Towards robust data-driven automated recovery of symbolic conservation laws from limited data 从有限数据中实现稳健的数据驱动自动恢复符号守恒定律

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-08-04 DOI: 10.1088/2632-2153/ad6390

Tracey Oellerich and Maria Emelianenko

引用次数: 0

Coincident learning for unsupervised anomaly detection of scientific instruments 用于科学仪器无监督异常检测的巧合学习

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-08-04 DOI: 10.1088/2632-2153/ad64a6

Ryan Humble, Zhe Zhang, Finn O’Shea, Eric Darve and Daniel Ratner

{"title":"Coincident learning for unsupervised anomaly detection of scientific instruments","authors":"Ryan Humble, Zhe Zhang, Finn O’Shea, Eric Darve and Daniel Ratner","doi":"10.1088/2632-2153/ad64a6","DOIUrl":"https://doi.org/10.1088/2632-2153/ad64a6","url":null,"abstract":"Anomaly detection is an important task for complex scientific experiments and other complex systems (e.g. industrial facilities, manufacturing), where failures in a sub-system can lead to lost data, poor performance, or even damage to components. While scientific facilities generate a wealth of data, labeled anomalies may be rare (or even nonexistent), and expensive to acquire. Unsupervised approaches are therefore common and typically search for anomalies either by distance or density of examples in the input feature space (or some associated low-dimensional representation). This paper presents a novel approach called coincident learning for anomaly detection (CoAD), which is specifically designed for multi-modal tasks and identifies anomalies based on coincident behavior across two different slices of the feature space. We define an unsupervised metric, , out of analogy to the supervised classification Fβ statistic. CoAD uses to train an anomaly detection algorithm on unlabeled data, based on the expectation that anomalous behavior in one feature slice is coincident with anomalous behavior in the other. The method is illustrated using a synthetic outlier data set and a MNIST-based image data set, and is compared to prior state-of-the-art on two real-world tasks: a metal milling data set and our motivating task of identifying RF station anomalies in a particle accelerator.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"76 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OmniJet-α: the first cross-task foundation model for particle physics OmniJet-α：首个用于粒子物理学的跨任务基础模型

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-08-01 DOI: 10.1088/2632-2153/ad66ad

Joschka Birk, Anna Hallin and Gregor Kasieczka

{"title":"OmniJet-α: the first cross-task foundation model for particle physics","authors":"Joschka Birk, Anna Hallin and Gregor Kasieczka","doi":"10.1088/2632-2153/ad66ad","DOIUrl":"https://doi.org/10.1088/2632-2153/ad66ad","url":null,"abstract":"Foundation models are multi-dataset and multi-task machine learning methods that once pre-trained can be fine-tuned for a large variety of downstream applications. The successful development of such general-purpose models for physics data would be a major breakthrough as they could improve the achievable physics performance while at the same time drastically reduce the required amount of training time and data. We report significant progress on this challenge on several fronts. First, a comprehensive set of evaluation methods is introduced to judge the quality of an encoding from physics data into a representation suitable for the autoregressive generation of particle jets with transformer architectures (the common backbone of foundation models). These measures motivate the choice of a higher-fidelity tokenization compared to previous works. Finally, we demonstrate transfer learning between an unsupervised problem (jet generation) and a classic supervised task (jet tagging) with our new OmniJet-α model. This is the first successful transfer between two different and actively studied classes of tasks and constitutes a major step in the building of foundation models for particle physics.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"81 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems 利用物理信息可逆神经网络对逆问题进行高效贝叶斯推理

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-22 DOI: 10.1088/2632-2153/ad5f74

Xiaofei Guan, Xintong Wang, Hao Wu, Zihao Yang and Peng Yu

{"title":"Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems","authors":"Xiaofei Guan, Xintong Wang, Hao Wu, Zihao Yang and Peng Yu","doi":"10.1088/2632-2153/ad5f74","DOIUrl":"https://doi.org/10.1088/2632-2153/ad5f74","url":null,"abstract":"This paper presents an innovative approach to tackle Bayesian inverse problems using physics-informed invertible neural networks (PI-INN). Serving as a neural operator model, PI-INN employs an invertible neural network (INN) to elucidate the relationship between the parameter field and the solution function in latent variable spaces. Specifically, the INN decomposes the latent variable of the parameter field into two distinct components: the expansion coefficients that represent the solution to the forward problem, and the noise that captures the inherent uncertainty associated with the inverse problem. Through precise estimation of the forward mapping and preservation of statistical independence between expansion coefficients and latent noise, PI-INN offers an accurate and efficient generative model for resolving Bayesian inverse problems, even in the absence of labeled data. For a given solution function, PI-INN can provide tractable and accurate estimates of the posterior distribution of the underlying parameter field. Moreover, capitalizing on the INN’s characteristics, we propose a novel independent loss function to effectively ensure the independence of the INN’s decomposition results. The efficacy and precision of the proposed PI-INN are demonstrated through a series of numerical experiments.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"214 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Datacube segmentation via deep spectral clustering 通过深度光谱聚类进行数据立方体分割

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-21 DOI: 10.1088/2632-2153/ad622f

Alessandro Bombini, Fernando García-Avello Bofías, Caterina Bracci, Michele Ginolfi and Chiara Ruberto

{"title":"Datacube segmentation via deep spectral clustering","authors":"Alessandro Bombini, Fernando García-Avello Bofías, Caterina Bracci, Michele Ginolfi and Chiara Ruberto","doi":"10.1088/2632-2153/ad622f","DOIUrl":"https://doi.org/10.1088/2632-2153/ad622f","url":null,"abstract":"Extended vision techniques are ubiquitous in physics. However, the data cubes steaming from such analysis often pose a challenge in their interpretation, due to the intrinsic difficulty in discerning the relevant information from the spectra composing the data cube. Furthermore, the huge dimensionality of data cube spectra poses a complex task in its statistical interpretation; nevertheless, this complexity contains a massive amount of statistical information that can be exploited in an unsupervised manner to outline some essential properties of the case study at hand, e.g. it is possible to obtain an image segmentation via (deep) clustering of data-cube’s spectra, performed in a suitably defined low-dimensional embedding space. To tackle this topic, we explore the possibility of applying unsupervised clustering methods in encoded space, i.e. perform deep clustering on the spectral properties of datacube pixels. A statistical dimensional reduction is performed by an ad hoc trained (variational) AutoEncoder, in charge of mapping spectra into lower dimensional metric spaces, while the clustering process is performed by a (learnable) iterative K-means clustering algorithm. We apply this technique to two different use cases, of different physical origins: a set of macro mapping x-ray fluorescence (MA-XRF) synthetic data on pictorial artworks, and a dataset of simulated astrophysical observations.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"32 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Causal hybrid modeling with double machine learning—applications in carbon flux modeling 双机器学习的因果混合建模--在碳通量建模中的应用

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-18 DOI: 10.1088/2632-2153/ad5a60

Kai-Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais, Markus Reichstein and Gustau Camps-Valls

{"title":"Causal hybrid modeling with double machine learning—applications in carbon flux modeling","authors":"Kai-Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais, Markus Reichstein and Gustau Camps-Valls","doi":"10.1088/2632-2153/ad5a60","DOIUrl":"https://doi.org/10.1088/2632-2153/ad5a60","url":null,"abstract":"Hybrid modeling integrates machine learning with scientific knowledge to enhance interpretability, generalization, and adherence to natural laws. Nevertheless, equifinality and regularization biases pose challenges in hybrid modeling to achieve these purposes. This paper introduces a novel approach to estimating hybrid models via a causal inference framework, specifically employing double machine learning (DML) to estimate causal effects. We showcase its use for the Earth sciences on two problems related to carbon dioxide fluxes. In the Q10 model, we demonstrate that DML-based hybrid modeling is superior in estimating causal parameters over end-to-end deep neural network approaches, proving efficiency, robustness to bias from regularization methods, and circumventing equifinality. Our approach, applied to carbon flux partitioning, exhibits flexibility in accommodating heterogeneous causal effects. The study emphasizes the necessity of explicitly defining causal graphs and relationships, advocating for this as a general best practice. We encourage the continued exploration of causality in hybrid models for more interpretable and trustworthy results in knowledge-guided machine learning.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"18 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Retrieving past quantum features with deep hybrid classical-quantum reservoir computing 利用深度混合经典-量子存储计算检索过去的量子特征

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-18 DOI: 10.1088/2632-2153/ad5f12

Johannes Nokkala, Gian Luca Giorgi and Roberta Zambrini

引用次数: 0

Ultrafast jet classification at the HL-LHC 超高速大型强子对撞机的超快射流分类

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-17 DOI: 10.1088/2632-2153/ad5f10

Patrick Odagiu, Zhiqiang Que, Javier Duarte, Johannes Haller, Gregor Kasieczka, Artur Lobanov, Vladimir Loncar, Wayne Luk, Jennifer Ngadiuba, Maurizio Pierini, Philipp Rincke, Arpita Seksaria, Sioni Summers, Andre Sznajder, Alexander Tapper and Thea K Årrestad

引用次数: 0

Quantum extreme learning of molecular potential energy surfaces and force fields 分子势能面和力场的量子极端学习

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-17 DOI: 10.1088/2632-2153/ad6120

Gabriele Lo Monaco, Marco Bertini, Salvatore Lorenzo and G Massimo Palma

{"title":"Quantum extreme learning of molecular potential energy surfaces and force fields","authors":"Gabriele Lo Monaco, Marco Bertini, Salvatore Lorenzo and G Massimo Palma","doi":"10.1088/2632-2153/ad6120","DOIUrl":"https://doi.org/10.1088/2632-2153/ad6120","url":null,"abstract":"Quantum machine learning algorithms are expected to play a pivotal role in quantum chemistry simulations in the immediate future. One such key application is the training of a quantum neural network to learn the potential energy surface and force field of molecular systems. We address this task by using the quantum extreme learning machine paradigm. This particular supervised learning routine allows for resource-efficient training, consisting of a simple linear regression performed on a classical computer. We have tested a setup that can be used to study molecules of any dimension and is optimized for immediate use on NISQ devices with a limited number of native gates. We have applied this setup to three case studies: lithium hydride, water, and formamide, carrying out both noiseless simulations and actual implementation on IBM quantum hardware. Compared to other supervised learning routines, the proposed setup requires minimal quantum resources, making it feasible for direct implementation on quantum platforms, while still achieving a high level of predictive accuracy compared to simulations. Our encouraging results pave the way towards the future application to more complex molecules, being the proposed setup scalable.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"19 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solving deep-learning density functional theory via variational autoencoders 通过变分自动编码器解决深度学习密度泛函理论问题

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-17 DOI: 10.1088/2632-2153/ad611f

Emanuele Costa, Giuseppe Scriva and Sebastiano Pilati

{"title":"Solving deep-learning density functional theory via variational autoencoders","authors":"Emanuele Costa, Giuseppe Scriva and Sebastiano Pilati","doi":"10.1088/2632-2153/ad611f","DOIUrl":"https://doi.org/10.1088/2632-2153/ad611f","url":null,"abstract":"In recent years, machine learning models, chiefly deep neural networks, have revealed suited to learn accurate energy-density functionals from data. However, problematic instabilities have been shown to occur in the search of ground-state density profiles via energy minimization. Indeed, any small noise can lead astray from realistic profiles, causing the failure of the learned functional and, hence, strong violations of the variational property. In this article, we employ variational autoencoders (VAEs) to build a compressed, flexible, and regular representation of the ground-state density profiles of various quantum models. Performing energy minimization in this compressed space allows us to avoid both numerical instabilities and variational biases due to excessive constraints. Our tests are performed on one-dimensional single-particle models from the literature in the field and, notably, on a three-dimensional disordered potential. In all cases, the ground-state energies are estimated with errors below the chemical accuracy and the density profiles are accurately reproduced without numerical artifacts. Furthermore, we show that it is possible to perform transfer learning, applying pre-trained VAEs to different potentials.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"286 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0