Machine Learning Science and Technology最新文献_第8页

Finetuning foundation models for joint analysis optimization in High Energy Physics 微调高能物理联合分析优化的基础模型

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-20 DOI: 10.1088/2632-2153/ad55a3

Matthias Vigl, Nicole Hartman and Lukas Heinrich

引用次数: 0

Sparse autoregressive neural networks for classical spin systems 经典自旋系统的稀疏自回归神经网络

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-20 DOI: 10.1088/2632-2153/ad5783

Indaco Biazzo, Dian Wu and Giuseppe Carleo

{"title":"Sparse autoregressive neural networks for classical spin systems","authors":"Indaco Biazzo, Dian Wu and Giuseppe Carleo","doi":"10.1088/2632-2153/ad5783","DOIUrl":"https://doi.org/10.1088/2632-2153/ad5783","url":null,"abstract":"Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this domain. However, these neural networks are often treated as black boxes, with architectures primarily influenced by data-driven problems in computational science. Addressing this gap, we introduce a novel autoregressive neural network architecture named TwoBo, specifically designed for sparse two-body interacting spin systems. We directly incorporate the Boltzmann distribution into its architecture and parameters, resulting in enhanced convergence speed, superior free energy accuracy, and reduced trainable parameters. We perform numerical experiments on disordered, frustrated systems with more than 1000 spins on grids and random graphs, and demonstrate its advantages compared to previous autoregressive and recurrent architectures. Our findings validate a physically informed approach and suggest potential extensions to multivalued variables and many-body interaction systems, paving the way for broader applications in scientific research.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"46 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141531276","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

Merging automatic differentiation and the adjoint method for photonic inverse design 将自动微分法与光子逆向设计的邻接法结合起来

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-20 DOI: 10.1088/2632-2153/ad5411

Alexander Luce, Rasoul Alaee, Fabian Knorr and Florian Marquardt

{"title":"Merging automatic differentiation and the adjoint method for photonic inverse design","authors":"Alexander Luce, Rasoul Alaee, Fabian Knorr and Florian Marquardt","doi":"10.1088/2632-2153/ad5411","DOIUrl":"https://doi.org/10.1088/2632-2153/ad5411","url":null,"abstract":"Optimizing the shapes and topology of physical devices is crucial for both scientific and technological advancements, given their wide-ranging implications across numerous industries and research areas. Innovations in shape and topology optimization have been observed across a wide range of fields, notably structural mechanics, fluid mechanics, and more recently, photonics. Gradient-based inverse design techniques have been particularly successful for photonic and optical problems, resulting in integrated, miniaturized hardware that has set new standards in device performance. To calculate the gradients, there are typically two approaches: namely, either by implementing specialized solvers using automatic differentiation (AD) or by deriving analytical solutions for gradient calculation and adjoint sources by hand. In this work, we propose a middle ground and present a hybrid approach that leverages and enables the benefits of AD for handling gradient derivation while using existing, proven but black-box photonic solvers for numerical solutions. Utilizing the adjoint method, we make existing numerical solvers differentiable and seamlessly integrate them into an AD framework. Further, this enables users to integrate the optimization environment seamlessly with other autodifferentiable components such as machine learning, geometry generation, or intricate post-processing which could lead to better photonic design workflows. We illustrate the approach through two distinct photonic optimization problems: optimizing the Purcell factor of a magnetic dipole in the vicinity of an optical nanocavity and enhancing the light extraction efficiency of a µLED.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"12 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504573","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

Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets 扭转范德华磁体中哈密顿参数估计和磁域图像生成的深度学习方法

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-19 DOI: 10.1088/2632-2153/ad56fa

Woo Seok Lee, Taegeun Song and Kyoung-Min Kim

{"title":"Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets","authors":"Woo Seok Lee, Taegeun Song and Kyoung-Min Kim","doi":"10.1088/2632-2153/ad56fa","DOIUrl":"https://doi.org/10.1088/2632-2153/ad56fa","url":null,"abstract":"The application of twist engineering in van der Waals magnets has opened new frontiers in the field of two-dimensional magnetism, yielding distinctive magnetic domain structures. Despite the introduction of numerous theoretical methods, limitations persist in terms of accuracy or efficiency due to the complex nature of the magnetic Hamiltonians pertinent to these systems. In this study, we introduce a deep-learning approach to tackle these challenges. Utilizing customized, fully connected networks, we develop two deep-neural-network kernels that facilitate efficient and reliable analysis of twisted van der Waals magnets. Our regression model is adept at estimating the magnetic Hamiltonian parameters of twisted bilayer CrI3 from its magnetic domain images generated through atomistic spin simulations. The ‘generative model’ excels in producing precise magnetic domain images from the provided magnetic parameters. The trained networks for these models undergo thorough validation, including statistical error analysis and assessment of robustness against noisy injections. These advancements not only extend the applicability of deep-learning methods to twisted van der Waals magnets but also streamline future investigations into these captivating yet poorly understood systems.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"86 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519231","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

Machine learning meets Kepler: inverting Kepler’s equation for All vs All conjunction analysis 机器学习与开普勒：倒转开普勒方程进行全局与全局连线分析

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-13 DOI: 10.1088/2632-2153/ad51cc

Kevin Otto, Simon Burgis, Kristian Kersting, Reinhold Bertrand and Devendra Singh Dhami

{"title":"Machine learning meets Kepler: inverting Kepler’s equation for All vs All conjunction analysis","authors":"Kevin Otto, Simon Burgis, Kristian Kersting, Reinhold Bertrand and Devendra Singh Dhami","doi":"10.1088/2632-2153/ad51cc","DOIUrl":"https://doi.org/10.1088/2632-2153/ad51cc","url":null,"abstract":"The number of satellites in orbit around Earth is increasing rapidly, with the risk of collision rising accordingly. Trends of the global population of satellites need to be analyzed to test the viability and impact of proposed rules and laws affecting the satellite population and collision avoidance strategies. This requires large scale simulations of satellites that are propagated on long timescales to compute the large amounts of actionable close encounters (called conjunctions), which could lead to collisions. Rigorously checking for conjunctions by computing future states of orbits is computationally expensive due to the large amount of objects involved and conjunction filters are thus used to remove non-conjuncting orbit pairs from the list of possible conjunctions. In this work, we explore the possibility of machine learning (ML) based conjunction filters using several algorithms such as eXtreme Gradient Boosting, TabNet and (physics-informed) neural networks and deep operator networks. To show the viability and the potential of ML based filters, these algorithms are trained to predict the future state of orbits. For the physics-informed approaches, multiple partial differential equations are set up using the Kepler equation as a basis. The empirical results demonstrate that physics-informed deep operator networks are capable of predicting the future state of orbits using these equations (RMSE: 0.136) and outperform eXtreme Gradient Boosting (RMSE: 0.568) and TabNet (RMSE: 0.459). We also propose a filter based on the trained deep operator network which is shown to outperforms the filter capability of the commonly used perigee-apogee test and the orbit path filter on a synthetic dataset, while being on average 3.2 times faster to compute than a rigorous conjunction check.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"34 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519160","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

STG-MTL: scalable task grouping for multi-task learning using data maps STG-MTL：利用数据图谱对多任务学习进行可扩展的任务分组

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-13 DOI: 10.1088/2632-2153/ad4e04

Ammar Sherif, Abubakar Abid, Mustafa Elattar and Mohamed ElHelw

{"title":"STG-MTL: scalable task grouping for multi-task learning using data maps","authors":"Ammar Sherif, Abubakar Abid, Mustafa Elattar and Mohamed ElHelw","doi":"10.1088/2632-2153/ad4e04","DOIUrl":"https://doi.org/10.1088/2632-2153/ad4e04","url":null,"abstract":"Multi-Task Learning (MTL) is a powerful technique that has gained popularity due to its performance improvement over traditional Single-Task Learning (STL). However, MTL is often challenging because there is an exponential number of possible task groupings, which can make it difficult to choose the best one because some groupings might produce performance degradation due to negative interference between tasks. That is why existing solutions are severely suffering from scalability issues, limiting any practical application. In our paper, we propose a new data-driven method that addresses these challenges and provides a scalable and modular solution for classification task grouping based on a re-proposed data-driven features, Data Maps, which capture the training dynamics for each classification task during the MTL training. Through a theoretical comparison with other techniques, we manage to show that our approach has the superior scalability. Our experiments show a better performance and verify the method’s effectiveness, even on an unprecedented number of tasks (up to 100 tasks on CIFAR100). Being the first to work on such number of tasks, our comparisons on the resulting grouping shows similar grouping to the mentioned in the dataset, CIFAR100. Finally, we provide a modular implementation3for easier integration and testing, with examples from multiple datasets and tasks.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"10 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519159","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

Synergizing human expertise and AI efficiency with language model for microscopy operation and automated experiment design * 通过显微镜操作和自动实验设计语言模型，实现人类专业知识与人工智能效率的协同 *

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-12 DOI: 10.1088/2632-2153/ad52e9

Yongtao Liu, Marti Checa and Rama K Vasudevan

{"title":"Synergizing human expertise and AI efficiency with language model for microscopy operation and automated experiment design *","authors":"Yongtao Liu, Marti Checa and Rama K Vasudevan","doi":"10.1088/2632-2153/ad52e9","DOIUrl":"https://doi.org/10.1088/2632-2153/ad52e9","url":null,"abstract":"With the advent of large language models (LLMs), in both the open source and proprietary domains, attention is turning to how to exploit such artificial intelligence (AI) systems in assisting complex scientific tasks, such as material synthesis, characterization, analysis and discovery. Here, we explore the utility of LLMs, particularly ChatGPT4, in combination with application program interfaces (APIs) in tasks of experimental design, programming workflows, and data analysis in scanning probe microscopy, using both in-house developed APIs and APIs given by a commercial vendor for instrument control. We find that the LLM can be especially useful in converting ideations of experimental workflows to executable code on microscope APIs. Beyond code generation, we find that the GPT4 is capable of analyzing microscopy images in a generic sense. At the same time, we find that GPT4 suffers from an inability to extend beyond basic analyses for more in-depth technical experimental design. We argue that an LLM specifically fine-tuned for individual scientific domains can potentially be a better language interface for converting scientific ideations from human experts to executable workflows. Such a synergy between human expertise and LLM efficiency in experimentation can open new doors for accelerating scientific research, enabling effective experimental protocols sharing in the scientific community.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"39 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519161","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

Investigating the ability of PINNs to solve Burgers’ PDE near finite-time blowup 研究 PINNs 在有限时间爆炸附近求解布尔格斯 PDE 的能力

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-11 DOI: 10.1088/2632-2153/ad51cd

Dibyakanti Kumar, Anirbit Mukherjee

{"title":"Investigating the ability of PINNs to solve Burgers’ PDE near finite-time blowup","authors":"Dibyakanti Kumar, Anirbit Mukherjee","doi":"10.1088/2632-2153/ad51cd","DOIUrl":"https://doi.org/10.1088/2632-2153/ad51cd","url":null,"abstract":"Physics Informed Neural Networks (PINNs) have been achieving ever newer feats of solving complicated Partial Differential Equations (PDEs) numerically while offering an attractive trade-off between accuracy and speed of inference. A particularly challenging aspect of PDEs is that there exist simple PDEs which can evolve into singular solutions in finite time starting from smooth initial conditions. In recent times some striking experiments have suggested that PINNs might be good at even detecting such finite-time blow-ups. In this work, we embark on a program to investigate this stability of PINNs from a rigorous theoretical viewpoint. Firstly, we derive error bounds for PINNs for Burgers’ PDE, in arbitrary dimensions, under conditions that allow for a finite-time blow-up. Our bounds give a theoretical justification for the functional regularization terms that have been reported to be useful for training PINNs near finite-time blow-up. Then we demonstrate via experiments that our bounds are significantly correlated to the <inline-formula>\u0000<tex-math><?CDATA $ell_2$?></tex-math>\u0000<mml:math overflow=\"scroll\"><mml:mrow><mml:msub><mml:mi>ℓ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math>\u0000<inline-graphic xlink:href=\"mlstad51cdieqn1.gif\" xlink:type=\"simple\"></inline-graphic>\u0000</inline-formula>-distance of the neurally found surrogate from the true blow-up solution, when computed on sequences of PDEs that are getting increasingly close to a blow-up.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"134 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141519162","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

Reinforcement learning pulses for transmon qubit entangling gates 用于跨文量子比特纠缠门的强化学习脉冲

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-11 DOI: 10.1088/2632-2153/ad4f4d

Ho Nam Nguyen, Felix Motzoi, Mekena Metcalf, K Birgitta Whaley, Marin Bukov and Markus Schmitt

{"title":"Reinforcement learning pulses for transmon qubit entangling gates","authors":"Ho Nam Nguyen, Felix Motzoi, Mekena Metcalf, K Birgitta Whaley, Marin Bukov and Markus Schmitt","doi":"10.1088/2632-2153/ad4f4d","DOIUrl":"https://doi.org/10.1088/2632-2153/ad4f4d","url":null,"abstract":"The utility of a quantum computer is highly dependent on the ability to reliably perform accurate quantum logic operations. For finding optimal control solutions, it is of particular interest to explore model-free approaches, since their quality is not constrained by the limited accuracy of theoretical models for the quantum processor—in contrast to many established gate implementation strategies. In this work, we utilize a continuous control reinforcement learning algorithm to design entangling two-qubit gates for superconducting qubits; specifically, our agent constructs cross-resonance and CNOT gates without any prior information about the physical system. Using a simulated environment of fixed-frequency fixed-coupling transmon qubits, we demonstrate the capability to generate novel pulse sequences that outperform the standard cross-resonance gates in both fidelity and gate duration, while maintaining a comparable susceptibility to stochastic unitary noise. We further showcase an augmentation in training and input information that allows our agent to adapt its pulse design abilities to drifting hardware characteristics, importantly, with little to no additional optimization. Our results exhibit clearly the advantages of unbiased adaptive-feedback learning-based optimization methods for transmon gate design.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"6 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504575","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

A quantum inspired approach to learning dynamical laws from data—block-sparsity and gauge-mediated weight sharing 从数据块稀疏性和规中介权重共享中学习动力学规律的量子启发式方法

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-11 DOI: 10.1088/2632-2153/ad4f4e

J Fuksa, M Götte, I Roth, J Eisert

{"title":"A quantum inspired approach to learning dynamical laws from data—block-sparsity and gauge-mediated weight sharing","authors":"J Fuksa, M Götte, I Roth, J Eisert","doi":"10.1088/2632-2153/ad4f4e","DOIUrl":"https://doi.org/10.1088/2632-2153/ad4f4e","url":null,"abstract":"Recent years have witnessed an increased interest in recovering dynamical laws of complex systems in a largely data-driven fashion under meaningful hypotheses. In this work, we propose a scalable and numerically robust method for this task, utilizing efficient block-sparse tensor train representations of dynamical laws, inspired by similar approaches in quantum many-body systems. Low-rank tensor train representations have been previously derived for dynamical laws of one-dimensional systems. We extend this result to efficient representations of systems with <italic toggle=\"yes\">K</italic>-mode interactions and controlled approximations of systems with decaying interactions. We further argue that natural structure assumptions on dynamical laws, such as bounded polynomial degrees, can be exploited in the form of block-sparse support patterns of tensor-train cores. Additional structural similarities between interactions of certain modes can be accounted for by weight sharing within the ansatz. To make use of these structure assumptions, we propose a novel optimization algorithm, block-sparsity restricted alternating least squares with gauge-mediated weight sharing. The algorithm is inspired by similar notions in machine learning and achieves a significant improvement in performance over previous approaches. We demonstrate the performance of the method numerically on three one-dimensional systems—the Fermi–Pasta–Ulam–Tsingou system, rotating magnetic dipoles and point particles interacting via modified Lennard–Jones potentials, observing a highly accurate and noise-robust recovery.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"84 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141531277","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