Physical Review E最新文献_第3页

Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal. 利用不确定性感知潜在演化反转的时空光束动力学时间反演。

IF 2.2 3区物理与天体物理

Physical Review E Pub Date : 2025-02-01 DOI: 10.1103/PhysRevE.111.025307

Mahindra Rautela, Alan Williams, Alexander Scheinker

{"title":"Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal.","authors":"Mahindra Rautela, Alan Williams, Alexander Scheinker","doi":"10.1103/PhysRevE.111.025307","DOIUrl":"https://doi.org/10.1103/PhysRevE.111.025307","url":null,"abstract":"Charged particle dynamics under the influence of electromagnetic fields is a challenging spatiotemporal problem. Many high-performance physics-based simulators for predicting behavior in a charged particle beam are computationally expensive, limiting their utility for solving inverse problems online. The problem of estimating upstream six-dimensional (6D) phase space given downstream measurements of charged particles in an accelerator is an inverse problem of growing importance. This paper introduces a reverse latent evolution model designed for the temporal inversion of forward beam dynamics. In this two-step self-supervised deep learning framework, we utilize a conditional variational autoencoder (CVAE) to project 6D phase space projections of a charged particle beam into a lower-dimensional latent distribution. Subsequently, we autoregressively learn the inverse temporal dynamics in the latent space using a long short-term memory (LSTM) network. The coupled CVAE-LSTM framework can predict 6D phase space projections across all upstream accelerating sections based on single or multiple downstream phase space measurements as inputs. The proposed model also captures the aleatoric uncertainty of the high-dimensional input data within the latent space. This uncertainty, which reflects potential uncertain measurements at a given module, is propagated through the LSTM network to estimate uncertainty bounds for all upstream predictions, demonstrating the robustness of the LSTM network to random perturbations in the input.","PeriodicalId":48698,"journal":{"name":"Physical Review E","volume":"111 2-2","pages":"025307"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Enhancing Otto refrigerator performance with a precooling strategy. 提高奥托冰箱性能与预冷策略。

IF 2.2 3区物理与天体物理

Physical Review E Pub Date : 2025-02-01 DOI: 10.1103/PhysRevE.111.L022101

Yang Xu, Huilin Ruan, Shaolin Luo, Shouhui Guo, Xian He, Jianhui Wang

引用次数: 0

Emergence in kinetic roughening with long-range temporal correlations. 具有长期时间相关性的动力学粗化的出现。

IF 2.2 3区物理与天体物理

Physical Review E Pub Date : 2025-02-01 DOI: 10.1103/PhysRevE.111.024124

Shuting Wang, Hui Xia

引用次数: 0

Discrete generative diffusion models without stochastic differential equations: A tensor network approach. 无随机微分方程的离散生成扩散模型：张量网络方法。

IF 2.2 3区物理与天体物理

Physical Review E Pub Date : 2025-02-01 DOI: 10.1103/PhysRevE.111.025302

Luke Causer, Grant M Rotskoff, Juan P Garrahan

{"title":"Discrete generative diffusion models without stochastic differential equations: A tensor network approach.","authors":"Luke Causer, Grant M Rotskoff, Juan P Garrahan","doi":"10.1103/PhysRevE.111.025302","DOIUrl":"https://doi.org/10.1103/PhysRevE.111.025302","url":null,"abstract":"Diffusion models (DMs) are a class of generative machine learning methods that sample a target distribution by transforming samples of a trivial (often Gaussian) distribution using a learned stochastic differential equation. In standard DMs, this is done by learning a \"score function\" that reverses the effect of adding diffusive noise to the distribution of interest. Here we consider the generalisation of DMs to lattice systems with discrete degrees of freedom, and where noise is added via Markov chain jump dynamics. We show how to use tensor networks (TNs) to efficiently define and sample such \"discrete diffusion models\" (DDMs) without explicitly having to solve a stochastic differential equation. We show the following: (i) by parametrising the data and evolution operators as TNs, the denoising dynamics can be represented exactly; (ii) the auto-regressive nature of TNs allows to generate samples efficiently and without bias; (iii) for sampling Boltzmann-like distributions, TNs allow to construct an efficient learning scheme that integrates well with Monte Carlo. We illustrate this approach to study the equilibrium of two models with non-trivial thermodynamics, the d=1 constrained Fredkin chain and the d=2 Ising model.","PeriodicalId":48698,"journal":{"name":"Physical Review E","volume":"111 2-2","pages":"025302"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Emergent flocking in mixtures of antialigning self-propelled particles. 在反对齐的自推进粒子混合物中出现的群集。

IF 2.2 3区物理与天体物理

Physical Review E Pub Date : 2025-02-01 DOI: 10.1103/PhysRevE.111.L023402

Rüdiger Kürsten, Jakob Mihatsch, Thomas Ihle

引用次数: 0

Individual particle persistence antagonizes global ordering in populations of nematically aligning self-propelled particles. 在向列排列的自推进粒子种群中，个体粒子持久性对抗全局有序。

IF 2.2 3区物理与天体物理

Physical Review E Pub Date : 2025-02-01 DOI: 10.1103/PhysRevE.111.025409

J M Nava-Sedeño, R Klages, H Hatzikirou, Francisco J Sevilla, A Deutsch

{"title":"Individual particle persistence antagonizes global ordering in populations of nematically aligning self-propelled particles.","authors":"J M Nava-Sedeño, R Klages, H Hatzikirou, Francisco J Sevilla, A Deutsch","doi":"10.1103/PhysRevE.111.025409","DOIUrl":"https://doi.org/10.1103/PhysRevE.111.025409","url":null,"abstract":"The transition from individual to collective motion plays a significant role in many biological processes. While the implications of different types of particle-particle interactions for the emergence of particular modes of collective motion have been well studied, it is unclear how particular types of individual migration patterns influence collective motion. Here, motivated by swarming bacteria Myxococcus xanthus, we investigate the combined effects of the individual pattern of migration and particle-particle interactions on the emergence of collective migration. We analyze the effects of a feature of individual pattern migration, the persistence of motion, on the collective properties of the system that emerge from interactions among individuals, particularly when nematic velocity alignment interaction mediates collective dynamics. We find, through computer simulations and mathematical analysis, that an initially disordered migratory state can become globally ordered by increasing either the particle-particle alignment interaction strength or the persistence of individual migration. In contrast, we find that persistence prevents the emergence of global nematic order when both persistence and nematic alignment are comparatively high. We conclude that behavior at the population level not only depends on interactions between individuals but also on their own intrinsic behavior.","PeriodicalId":48698,"journal":{"name":"Physical Review E","volume":"111 2-2","pages":"025409"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Resolvent-based optimization for approximating the statistics of a chaotic Lorenz system. 混沌洛伦兹系统统计量近似的求解优化。

IF 2.2 3区物理与天体物理

Physical Review E Pub Date : 2025-02-01 DOI: 10.1103/PhysRevE.111.025104

Thomas Burton, Sean Symon, Ati S Sharma, Davide Lasagna

{"title":"Resolvent-based optimization for approximating the statistics of a chaotic Lorenz system.","authors":"Thomas Burton, Sean Symon, Ati S Sharma, Davide Lasagna","doi":"10.1103/PhysRevE.111.025104","DOIUrl":"https://doi.org/10.1103/PhysRevE.111.025104","url":null,"abstract":"We propose a framework for approximating the statistical properties of turbulent flows by combining variational methods for the search of unstable periodic orbits with resolvent analysis for dimensionality reduction. Traditional approaches relying on identifying all short, fundamental unstable periodic orbits to compute ergodic averages via cycle expansion are computationally prohibitive for high-dimensional fluid systems. Our framework stems from the observation in Lasagna [D. Lasagna, Phys. Rev. E 102, 052220 (2020)2470-004510.1103/PhysRevE.102.052220] that a single unstable periodic orbit with a period sufficiently long to span a large fraction of the attractor captures the statistical properties of chaotic trajectories. Given the difficulty of identifying unstable periodic orbits for high-dimensional fluid systems, approximate trajectories residing in a low-dimensional subspace are instead constructed using resolvent modes, which inherently capture the temporal periodicity of unstable periodic orbits. The amplitude coefficients of these modes are adjusted iteratively with gradient-based optimization to minimize the violation of the projected governing equations, producing trajectories that approximate, rather than exactly solve, the system dynamics. An attempt at utilizing this framework on a chaotic system is made here on the Lorenz 1963 equations, where resolvent analysis enables an exact dimensionality reduction from three to two dimensions. Key observables averaged over these trajectories produced by the approach as well as probability distributions and spectra rapidly converge to values obtained from long chaotic simulations, even with a limited number of iterations. This indicates that exact solutions may not be necessary to approximate the system's statistical behavior, as the trajectories obtained from partial optimization provide a sufficient \"sketch\" of the attractor in state space.","PeriodicalId":48698,"journal":{"name":"Physical Review E","volume":"111 2-2","pages":"025104"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Phase determination with and without deep learning. 有和没有深度学习的阶段确定。

IF 2.2 3区物理与天体物理

Physical Review E Pub Date : 2025-02-01 DOI: 10.1103/PhysRevE.111.024131

Burak Çivitcioğlu, Rudolf A Römer, Andreas Honecker

{"title":"Phase determination with and without deep learning.","authors":"Burak Çivitcioğlu, Rudolf A Römer, Andreas Honecker","doi":"10.1103/PhysRevE.111.024131","DOIUrl":"https://doi.org/10.1103/PhysRevE.111.024131","url":null,"abstract":"Detection of phase transitions is a critical task in statistical physics, traditionally pursued through analytic methods and direct numerical simulations. Recently, machine-learning techniques have emerged as promising tools in this context, with a particular focus on supervised and unsupervised learning methods, along with nonlearning approaches. In this paper, we study the performance of unsupervised learning in detecting phase transitions in the J_{1}-J_{2} Ising model on the square lattice. The model is chosen due to its simplicity and complexity, thus providing an understanding of the application of machine-learning techniques in both straightforward and challenging scenarios. We propose a simple method based on a direct comparison of configurations. The reconstruction error, defined as the mean-squared distance between two configurations, is used to determine the critical temperatures. The results from the comparison of configurations are contrasted with those of the configurations generated by variational autoencoders. Our findings highlight that for certain systems a simpler method can yield results comparable to more complex neural networks. This paper contributes to the broader understanding of machine-learning applications in statistical physics and introduces an efficient approach to the detection of phase transitions using machine determination techniques.","PeriodicalId":48698,"journal":{"name":"Physical Review E","volume":"111 2-1","pages":"024131"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Scaling behavior in the number theoretic division model of self-organized criticality. 自组织临界数论划分模型中的尺度行为。

IF 2.2 3区物理与天体物理

Physical Review E Pub Date : 2025-02-01 DOI: 10.1103/PhysRevE.111.024108

Rahul Chhimpa, Avinash Chand Yadav

引用次数: 0

Forecasting and decisions in the birth-death-suppression Markov model for wildfires. 野火出生-死亡-抑制马尔可夫模型中的预测和决策。

IF 2.2 3区物理与天体物理

Physical Review E Pub Date : 2025-02-01 DOI: 10.1103/PhysRevE.111.024318

George Hulsey, David L Alderson, Jean Carlson

引用次数: 0