Physical Review EPub Date : 2025-02-01DOI: 10.1103/PhysRevE.111.024124
Shuting Wang, Hui Xia
{"title":"Emergence in kinetic roughening with long-range temporal correlations.","authors":"Shuting Wang, Hui Xia","doi":"10.1103/PhysRevE.111.024124","DOIUrl":"https://doi.org/10.1103/PhysRevE.111.024124","url":null,"abstract":"<p><p>The role of long-range temporal correlations in kinetic roughening processes is significant, potentially influencing surface morphologies and dynamic scaling properties. This type of correlation, acting as a nonlocal interaction during growth processes, disrupts the self-affine fractal structure by causing the emergence of nontrivial global properties from individual local interactions. In this work, two typical discrete growth models, including random deposition and ballistic deposition, are investigated numerically in the presence of long-range temporal correlations. Two quantitative measurement methods, the Hellinger distance and the novelty detection, are used independently to determine whether emergence occurs. Our simulation results indicate that, above a critical threshold of temporal correlation exponent, the systems exhibit emergence behavior, consistent with the existence of nontrivial scaling properties and surface morphologies when long-range temporal correlations are presented.</p>","PeriodicalId":48698,"journal":{"name":"Physical Review E","volume":"111 2-1","pages":"024124"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657942","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}
Physical Review EPub Date : 2025-02-01DOI: 10.1103/PhysRevE.111.L023402
Rüdiger Kürsten, Jakob Mihatsch, Thomas Ihle
{"title":"Emergent flocking in mixtures of antialigning self-propelled particles.","authors":"Rüdiger Kürsten, Jakob Mihatsch, Thomas Ihle","doi":"10.1103/PhysRevE.111.L023402","DOIUrl":"https://doi.org/10.1103/PhysRevE.111.L023402","url":null,"abstract":"<p><p>We observe a flocking mechanism, the emergence of a state with global polar order, in mixed systems of self-propelled particles with purely antialigning interactions, i.e., the ground state for any pair of particles is to be opposedly oriented. In binary mixtures, we find that flocking can be realized by cross-species antialigning that is dominant compared to intraspecies antialignment. While the key mechanism can be understood within a mean-field description, beyond mean-field we develop an asymptotically exact Boltzmann-scattering theory from first principles. This theory yields analytical predictions for the flocking transition and shows excellent quantitative agreement with simulations of dilute systems. For large systems, we find either microphase separation or static patterns with patches or stripes that carry different polarization orientations.</p>","PeriodicalId":48698,"journal":{"name":"Physical Review E","volume":"111 2","pages":"L023402"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659155","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}
Physical Review EPub Date : 2025-02-01DOI: 10.1103/PhysRevE.111.L022101
Yang Xu, Huilin Ruan, Shaolin Luo, Shouhui Guo, Xian He, Jianhui Wang
{"title":"Enhancing Otto refrigerator performance with a precooling strategy.","authors":"Yang Xu, Huilin Ruan, Shaolin Luo, Shouhui Guo, Xian He, Jianhui Wang","doi":"10.1103/PhysRevE.111.L022101","DOIUrl":"https://doi.org/10.1103/PhysRevE.111.L022101","url":null,"abstract":"<p><p>The precooling strategy, which leads to exponentially faster heating, is a counterintuitive relaxation phenomenon wherein cooling the system before heating it dramatically shortens the relaxation time. We investigate the performance of a Markovian system functioning as an Otto refrigerator for a finite time, incorporating a precooling stage before the cyclic heating process. Our results demonstrate that precooling prior to the heating process in the Otto cycle optimizes the machine's performance by significantly enhancing the machine performance and the stability of the refrigerator.</p>","PeriodicalId":48698,"journal":{"name":"Physical Review E","volume":"111 2","pages":"L022101"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659169","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}
Physical Review EPub Date : 2025-02-01DOI: 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":"<p><p>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.</p>","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}
Physical Review EPub Date : 2025-02-01DOI: 10.1103/PhysRevE.111.024108
Rahul Chhimpa, Avinash Chand Yadav
{"title":"Scaling behavior in the number theoretic division model of self-organized criticality.","authors":"Rahul Chhimpa, Avinash Chand Yadav","doi":"10.1103/PhysRevE.111.024108","DOIUrl":"https://doi.org/10.1103/PhysRevE.111.024108","url":null,"abstract":"<p><p>We revisit the number theoretic division model of self-organized criticality [B. Luque et al.Phys. Rev. Lett. 101, 158702 (2008)10.1103/PhysRevLett.101.158702]. The model consists of a pool of M-1 ordered integers {2,3,⋯,M}, and the aim is to dynamically form a primitive set of integers, where no number can be divided or divisible by others. Using extensive simulation studies and finite-size scaling method, we find the primitive set size fluctuations in the division model to show power spectral density of the form 1/f^{α} in the frequency regime 1/M≪f≪1/2 with α≈2 (different from α≈1.80(1) as reported previously) along with an additional scaling in terms of the system size ∼M^{b}. We also show similar power spectra properties for a class of random walks with a power-law distributed jump size (Lévy flights).</p>","PeriodicalId":48698,"journal":{"name":"Physical Review E","volume":"111 2-1","pages":"024108"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659259","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}
Physical Review EPub Date : 2025-02-01DOI: 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":"<p><p>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.</p>","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}
Physical Review EPub Date : 2025-02-01DOI: 10.1103/PhysRevE.111.024318
George Hulsey, David L Alderson, Jean Carlson
{"title":"Forecasting and decisions in the birth-death-suppression Markov model for wildfires.","authors":"George Hulsey, David L Alderson, Jean Carlson","doi":"10.1103/PhysRevE.111.024318","DOIUrl":"https://doi.org/10.1103/PhysRevE.111.024318","url":null,"abstract":"<p><p>As changing climates transform the landscape of wildfire management and suppression, agencies are faced with difficult resource allocation decisions. We analyze tradeoffs in temporal resource allocation using a simple but robust Markov model of a wildfire under suppression: the birth-death-suppression process. Though the model is not spatial, its stochastic nature and rich temporal structure make it broadly applicable in describing the dynamic evolution of a fire including ignition, the effect of adverse conditions, and the effect of external suppression. With strong analytical and numerical control of the probabilities of outcomes, we construct classes of processes which analogize common wildfire suppression scenarios and determine aspects of optimal suppression allocations. We model problems which include resource management in changing conditions, the effect of resource mobilization delay, and allocation under uncertainty about future events. Our results are consistent with modern resource management and suppression practices in wildland fire.</p>","PeriodicalId":48698,"journal":{"name":"Physical Review E","volume":"111 2-1","pages":"024318"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658936","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}
Physical Review EPub Date : 2025-02-01DOI: 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":"<p><p>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.</p>","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}
Physical Review EPub Date : 2025-02-01DOI: 10.1103/PhysRevE.111.024115
V V Ryazanov
{"title":"Application of boundary functionals of random processes in statistical physics.","authors":"V V Ryazanov","doi":"10.1103/PhysRevE.111.024115","DOIUrl":"https://doi.org/10.1103/PhysRevE.111.024115","url":null,"abstract":"<p><p>The potential applications of boundary functionals of random processes, such as the extreme values of these processes, the moment of first reaching a fixed level, the value of the process at the moment of reaching the level, the moment of reaching extreme values, the time the process stays above a fixed level, and other functionals, are considered for the description of physical, chemical, and biological problems. Definitions of these functionals are provided, and characteristic functions are presented for the model with an exponential distribution of incoming demands. A generalization of these limitations is also considered. The potential uses of boundary functionals are demonstrated through examples such as a unicyclic network with affinity A, an asymmetric random walk, nonlinear diffusion, two-level model, Brownian motion, and multiple diffusing particles with reversible target-binding kinetics.</p>","PeriodicalId":48698,"journal":{"name":"Physical Review E","volume":"111 2-1","pages":"024115"},"PeriodicalIF":2.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659428","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}
Physical Review EPub Date : 2025-02-01DOI: 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":"<p><p>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.</p>","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}