Machine Learning Science and Technology最新文献_第3页

Neural force functional for non-equilibrium many-body colloidal systems 非平衡多体胶体系统的神经力函数

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-09-02 DOI: 10.1088/2632-2153/ad7191

Toni Zimmermann, Florian Sammüller, Sophie Hermann, Matthias Schmidt, Daniel de las Heras

{"title":"Neural force functional for non-equilibrium many-body colloidal systems","authors":"Toni Zimmermann, Florian Sammüller, Sophie Hermann, Matthias Schmidt, Daniel de las Heras","doi":"10.1088/2632-2153/ad7191","DOIUrl":"https://doi.org/10.1088/2632-2153/ad7191","url":null,"abstract":"We combine power functional theory and machine learning to study non-equilibrium overdamped many-body systems of colloidal particles at the level of one-body fields. We first sample in steady state the one-body fields relevant for the dynamics from computer simulations of Brownian particles under the influence of randomly generated external fields. A neural network is then trained with this data to represent locally in space the formally exact functional mapping from the one-body density and velocity profiles to the one-body internal force field. The trained network is used to analyse the non-equilibrium superadiabatic force field and the transport coefficients such as shear and bulk viscosities. Due to the local learning approach, the network can be applied to systems much larger than the original simulation box in which the one-body fields are sampled. Complemented with the exact non-equilibrium one-body force balance equation and a continuity equation, the network yields viable predictions of the dynamics in time-dependent situations. Even though training is based on steady states only, the predicted dynamics is in good agreement with simulation results. A neural dynamical density functional theory can be straightforwardly implemented as a limiting case in which the internal force field is that of an equilibrium system. The framework is general and directly applicable to other many-body systems of interacting particles following Brownian dynamics.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"44 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197731","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

Triggering dark showers with conditional dual auto-encoders 利用有条件的双自动编码器触发暗阵雨

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-09-02 DOI: 10.1088/2632-2153/ad652b

Luca Anzalone, Simranjit Singh Chhibra, Benedikt Maier, Nadezda Chernyavskaya, Maurizio Pierini

{"title":"Triggering dark showers with conditional dual auto-encoders","authors":"Luca Anzalone, Simranjit Singh Chhibra, Benedikt Maier, Nadezda Chernyavskaya, Maurizio Pierini","doi":"10.1088/2632-2153/ad652b","DOIUrl":"https://doi.org/10.1088/2632-2153/ad652b","url":null,"abstract":"We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalies causing deviations in data with respect to expected background events. In this work, we perform a normal-only anomaly detection, which employs only background samples, to search for manifestations of a dark version of strong force applying (variational) auto-encoders on raw detector images, which are large and highly sparse, without leveraging any physics-based pre-processing or strong assumption on the signals. The proposed CoDAE has a dual-encoder design, which is general and can learn an auxiliary yet compact latent space through spatial conditioning, showing a neat improvement over competitive physics-based baselines and related approaches, therefore also reducing the gap with fully supervised models. It is the first time an unsupervised model is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as an accurate, fast, model-independent algorithm to deploy, e.g. in the real-time event triggering systems of large hadron collider experiments such as ATLAS and CMS.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"43 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197719","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

Identifying chaotic dynamics in noisy time series through multimodal deep neural networks 通过多模态深度神经网络识别噪声时间序列中的混沌动力学

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-08-29 DOI: 10.1088/2632-2153/ad7190

Alessandro Giuseppi, Danilo Menegatti, Antonio Pietrabissa

引用次数: 0

Chaotic attractor reconstruction using small reservoirs—the influence of topology 利用小型水库重构混沌吸引子--拓扑结构的影响

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-08-27 DOI: 10.1088/2632-2153/ad6ee8

Lina Jaurigue

{"title":"Chaotic attractor reconstruction using small reservoirs—the influence of topology","authors":"Lina Jaurigue","doi":"10.1088/2632-2153/ad6ee8","DOIUrl":"https://doi.org/10.1088/2632-2153/ad6ee8","url":null,"abstract":"Forecasting timeseries based upon measured data is needed in a wide range of applications and has been the subject of extensive research. A particularly challenging task is the forecasting of timeseries generated by chaotic dynamics. In recent years reservoir computing has been shown to be an effective method of forecasting chaotic dynamics and reconstructing chaotic attractors from data. In this work strides are made toward smaller and lower complexity reservoirs with the goal of improved hardware implementability and more reliable production of adequate surrogate models. We show that a reservoir of uncoupled nodes more reliably produces long term timeseries predictions than more complex reservoir topologies. We then link the improved attractor reconstruction of the uncoupled reservoir with smaller spectral radii of the resulting surrogate systems. These results indicate that, the node degree plays an important role in determining whether the desired dynamics will be stable in the autonomous surrogate system which is attained via closed-loop operation of the trained reservoir. In terms of hardware implementability, uncoupled nodes would allow for greater freedom in the hardware architecture because no complex coupling setups are needed and because, for uncoupled nodes, the system response is equivalent for space and time multiplexing.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"13 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197695","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

JefiAtten: an attention-based neural network model for solving Maxwell’s equations with charge and current sources JefiAtten：基于注意力的神经网络模型，用于求解带电荷源和电流源的麦克斯韦方程

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-08-23 DOI: 10.1088/2632-2153/ad6ee9

Ming-Yan Sun, Peng Xu, Jun-Jie Zhang, Tai-Jiao Du, Jian-Guo Wang

引用次数: 0

Data-driven sparse modeling of oscillations in plasma space propulsion 等离子体空间推进器振荡的数据驱动稀疏建模

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-08-23 DOI: 10.1088/2632-2153/ad6d29

Borja Bayón-Buján, Mario Merino

引用次数: 0

Active causal learning for decoding chemical complexities with targeted interventions 通过主动因果学习解码复杂化学物质，进行有针对性的干预

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-08-23 DOI: 10.1088/2632-2153/ad6feb

Zachary R Fox, Ayana Ghosh

{"title":"Active causal learning for decoding chemical complexities with targeted interventions","authors":"Zachary R Fox, Ayana Ghosh","doi":"10.1088/2632-2153/ad6feb","DOIUrl":"https://doi.org/10.1088/2632-2153/ad6feb","url":null,"abstract":"Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have become standard for predictions, but they face challenges when applied across different datasets due to reliance on correlations between molecular representation and target properties. These approaches typically depend on large datasets to capture the diversity within the chemical space, facilitating a more accurate approximation, interpolation, or extrapolation of the chemical behavior of molecules. In our research, we introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling with the use of a graph loss function. This method identifies the smallest subset of the dataset capable of encoding the most information representative of a much larger chemical space. The identified causal relations are then leveraged to conduct systematic interventions, optimizing the design task within a chemical space that the models have not encountered previously. While our implementation focused on the QM9 quantum-chemical dataset for a specific design task—finding molecules with a large dipole moment—our active causal learning approach, driven by intelligent sampling and interventions, holds potential for broader applications in molecular, materials design and discovery.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"68 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197696","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

Emergence of chemotactic strategies with multi-agent reinforcement learning 多代理强化学习催化策略的出现

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-08-21 DOI: 10.1088/2632-2153/ad5f73

Samuel Tovey, Christoph Lohrmann, Christian Holm

{"title":"Emergence of chemotactic strategies with multi-agent reinforcement learning","authors":"Samuel Tovey, Christoph Lohrmann, Christian Holm","doi":"10.1088/2632-2153/ad5f73","DOIUrl":"https://doi.org/10.1088/2632-2153/ad5f73","url":null,"abstract":"Reinforcement learning (RL) is a flexible and efficient method for programming micro-robots in complex environments. Here we investigate whether RL can provide insights into biological systems when trained to perform chemotaxis. Namely, whether we can learn about how intelligent agents process given information in order to swim towards a target. We run simulations covering a range of agent shapes, sizes, and swim speeds to determine if the physical constraints on biological swimmers, namely Brownian motion, lead to regions where reinforcement learners’ training fails. We find that the RL agents can perform chemotaxis as soon as it is physically possible and, in some cases, even before the active swimming overpowers the stochastic environment. We study the efficiency of the emergent policy and identify convergence in agent size and swim speeds. Finally, we study the strategy adopted by the RL algorithm to explain how the agents perform their tasks. To this end, we identify three emerging dominant strategies and several rare approaches taken. These strategies, whilst producing almost identical trajectories in simulation, are distinct and give insight into the possible mechanisms behind which biological agents explore their environment and respond to changing conditions.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"97 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197697","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

Quantum support vector data description for anomaly detection 用于异常检测的量子支持向量数据描述

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-08-21 DOI: 10.1088/2632-2153/ad6be8

Hyeondo Oh, Daniel K Park

引用次数: 0

Normalizing flows as an enhanced sampling method for atomistic supercooled liquids 作为原子论过冷液体强化取样方法的归一化流动

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-08-21 DOI: 10.1088/2632-2153/ad6ca0

Gerhard Jung, Giulio Biroli, Ludovic Berthier

{"title":"Normalizing flows as an enhanced sampling method for atomistic supercooled liquids","authors":"Gerhard Jung, Giulio Biroli, Ludovic Berthier","doi":"10.1088/2632-2153/ad6ca0","DOIUrl":"https://doi.org/10.1088/2632-2153/ad6ca0","url":null,"abstract":"Normalizing flows can transform a simple prior probability distribution into a more complex target distribution. Here, we evaluate the ability and efficiency of generative machine learning methods to sample the Boltzmann distribution of an atomistic model for glass-forming liquids. This is a notoriously difficult task, as it amounts to ergodically exploring the complex free energy landscape of a disordered and frustrated many-body system. We optimize a normalizing flow model to successfully transform high-temperature configurations of a dense liquid into low-temperature ones, near the glass transition. We perform a detailed comparative analysis with established enhanced sampling techniques developed in the physics literature to assess and rank the performance of normalizing flows against state-of-the-art algorithms. We demonstrate that machine learning methods are very promising, showing a large speedup over conventional molecular dynamics. Normalizing flows show performances comparable to parallel tempering and population annealing, while still falling far behind the swap Monte Carlo algorithm. Our study highlights the potential of generative machine learning models in scientific computing for complex systems, but also points to some of its current limitations and the need for further improvement.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"12 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197699","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