Classical Physical Models Research Articles

Quantization and the Preservation of Structure across Theory Change

Abstract This article presents a novel sense in which theoretical structure has been preserved across the transition from classical to quantum physics. I import mathematical tools from category theory that have been used for structural comparisons in the context of theoretical equivalence and apply these tools to new situations involving theory change. The structural preservation takes the form of a categorical equivalence between categories of models of classical and quantum physics. I situate the significance of this structural preservation in terms of prospects for theory construction in quantum physics.

Defining geometric gauge theory to accommodate particles, continua, and fields

Gauge theory underpins the quantum field theories of the standard model, and in a previous paper was shown via a geometric approach to describe classical electromagnetism in a form which approximates quantum electrodynamics. Here we formalize and generalize the notion of a geometric gauge theory, then apply this framework to classical physical models, including an improved Lagrangian for matter field electromagnetism. We find a remarkably consistent series of actions, with straightforward limits under which each previous one may be obtained. Ancillary benefits include a gauge-independent Galilean Lagrangian, a geometric interpretation for the unusual metric dependence of four-momentum, a modern treatment of the effects of worldline variation on the four-current, a gauge theory of gravity which includes a matter field, and consistent units for matter field electromagnetism.

Parameter identification by deep learning of a material model for granular media

Classical physical modeling with associated numerical simulation (model-based), and prognostic methods based on the analysis of large amounts of data (data-driven) are the two most common methods used for the mapping of complex physical processes. In recent years, the efficient combination of these approaches has become increasingly important. Continuum mechanics in the core consists of conservation equations that-in addition to the always-necessary specification of the process conditions-can be supplemented by phenomenological material models. The latter are an idealized image of the specific material behavior that can be determined experimentally, empirically, and based on a wealth of expert knowledge. The more complex the material, the more difficult the calibration is. This situation forms the starting point for this work’s hybrid data-driven and model-based approach for mapping a complex physical process in continuum mechanics. Specifically, we use data generated from a classical physical model by the MESHFREE software (MESHFREE Team in Fraunhofer ITWM & SCAI: MESHFREE. https://www.meshfree.eu, 2023) to train a Principal Component Analysis-based neural network (PCA-NN) for the task of parameter identification of the material model parameters. The obtained results highlight the potential of deep-learning-based hybrid models for determining parameters, which are the key to characterizing materials occurring naturally such as sand, soil, mud, or snow. The motivation for our research is the simulation of the interaction of vehicles with sand. However, the applicability of the presented methodology is not limited to this industrial use case. In geosciences, when predicting the runout zones of landslides or avalanches and evaluating corresponding protective measures, the parameterization of the respective material model is essential.

The Physics of Preference: Unravelling Imprecision of Human Preferences through Magnetisation Dynamics

Paradoxical decision-making behaviours such as preference reversal often arise from imprecise or noisy human preferences. Harnessing the physical principle of magnetisation reversal in ferromagnetic nanostructures, we developed a model that closely reflects human decision-making dynamics. Tested against a spectrum of psychological data, our model adeptly captures the complexities inherent in individual choices. This blend of physics and psychology paves the way for fresh perspectives on understanding the imprecision of human decision-making processes, extending the reach of the current classical and quantum physical models of human behaviour and decision making.

The Puzzling of Stefan–Boltzmann Law: Classical or Quantum Physics

Stefan–Boltzmann law, stating the fourth power temperature dependence of the radiation emission by a black-body, was empirically formulated by Stefan in 1874 by fitting existing experiments and theoretically validated by Boltzmann in 1884 on the basis of a classical physical model involving thermodynamics principles and the radiation pressure predicted by Maxwell equations. At first sight the electromagnetic (EM) gas assumed by Boltzmann and following Rayleigh (1900) identifiable as an ensemble of N classical normal-modes, looks like an extension of the classical model of the massive ideal-gas. Accordingly, for this EM gas the internal total energy, U, was assumed to be function of volume V and temperature T as [Formula: see text], and the equation of state was given by [Formula: see text], with P the radiation pressure. In addition, Boltzmann implicitly assumed that, for given values of V and T, U and the number of modes N would take finite values. However, from one hand these assumptions are not justified by Maxwell equations and classical statistics since, in vacuum (i.e., far from the EM sources), the values of N and U diverge, the so-called ultraviolet catastrophe introduced by Ehrenfest in 1911. From another hand, Boltzmann derivation of Stefan law is found to be macroscopically compatible with its derivation from quantum statistics announced by Planck in 1901. In this paper, we present a justification of this puzzling classical/quantum compatibility by noticing that the implicit assumptions made by Boltzmann is fully justified by Planck quantum statistics. Furthermore, we shed new light on the interpretation of recent classical simulations of a black body carried out by Wang, Casati, and Benenti in 2022 who found an analogous puzzling consistency between Stefan–Boltzmann law and their simulations to induce speculations on classical physics and black body radiation that are claimed to require a critical reconsideration of the role of classical physics for the understanding of quantum mechanics.

Prediction of lattice thermal conductivity with two-stage interpretable machine learning

Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have led to the inefficient development of thermoelectric materials. In this study, we proposed a two-stage machine learning framework with physical interpretability incorporating domain knowledge to calculate high/low thermal conductivity rapidly. Specifically, crystal graph convolutional neural network (CGCNN) is constructed to predict the fundamental physical parameters related to lattice thermal conductivity. Based on the above physical parameters, an interpretable machine learning model–sure independence screening and sparsifying operator (SISSO), is trained to predict the lattice thermal conductivity. We have predicted the lattice thermal conductivity of all available materials in the open quantum materials database (OQMD) (https://www.oqmd.org/). The proposed approach guides the next step of searching for materials with ultra-high or ultra-low lattice thermal conductivity and promotes the development of new thermal insulation materials and thermoelectric materials.

ПЕРЕМЕЩЕНИЯ В СТЕРЖНЕВЫХ СИСТЕМАХ ЗА ПРЕДЕЛОМ УПРУГОСТИ

One of the provisions of the method for calculating building structures by the limiting state is the satisfaction of the operational requirements in relation to the displacements of their elements under load. An urgent problem is their determination at the stage of elastic-plastic deformation of the material. A technique is proposed for its solution for the case of the Prandtl diagram. The core system is a two-span statically indeterminate beam. Its limiting state in terms of bearing capacity, as well as an intermediate stage of deformation, are considered. The introduction of the reduced moment makes it possible to extend the Mohr-Maxwell formula beyond the limit of linear elasticity. The use of classical physical models of solid mechanics leads to the solution of the problem in an analytical form. Experiments were carried out to test the theoretical results obtained by the proposed method. In one of them, a two-span beam with a span of 50 cm and a cross section of 3×0.42 cm made of duralumin was tested. The mechanical characteristics of the material were previously obtained. Displacements were measured with the help of indicators. The maximum load (428 N) in each of the spans of the beam was 80 % of the limit value. In this case, the beam had elastic-plastic regions. The experiment revealed the dependence of displacements on the load, acceptable for the form of deformation: a linear graph with Hooke's law and a curve in the presence of plastic deformations. Deviations from theoretical values were no more than 3.3 %.

Geometry of information: Classical and quantum aspects

In this article, we describe various aspects of categorification of the structures, appearing in information theory. These aspects include probabilistic models both of classical and quantum physics, emergence of F–manifolds, and motivic enrichments.

THEORETICAL ANALYSIS OF MEMBRANE AIR DEHUMIDIFICATION IN AIR CONDITIONING SYSTEMS

The paper presents a simulation of the process of removing moisture from the air in air conditioning systems using construction vapor-permeable membranes. The existing classical physical and mathematical models are considered and analyzed. Based on the analysis of classical models, a schematic and corresponding theoretical mathematical model of air dehumidification with a semipermeable building membrane with certain characteristics for possibilities of use in air conditioning systems is developed and presented. The data of calculations of moisture release from air for the developed model are given. As a result of analytical researches the correct data for possible use in air conditioning systems are received. Key words: air conditioning systems, air dehumidification, semipermeable membrane, permeability, membrane air dehumidification, convective flow, diffusion flow, moisture permeability.

Abstract PO-089: Morphokinetic single-cell analysis and machine learning as a tool to characterize breast cancer cell motility and response to therapy

Abstract Triple-negative breast cancer (TNBC) is a type of aggressive breast cancer lacking the expression of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor-2 (HER-2). TNBC patients have a high propensity for distant metastasis and limited treatment options. Cancer cell motility and invasion are fundamental steps in metastasis. MET, a tyrosine kinase receptor, and its ligand, Hepatocyte Growth Factor/Scatter Factor (HGF/SF), induce specific signal transduction in tumor cells leading to cell motility and metastasis. MET's induced tumorigenesis and metastatic processes make it an ideal target for anti-cancer therapy. We characterized the motility patterns of m-Cherry labeled TNBC cells (BT-549, MDA-MB-231) and ER-positive cells (MCF7) subjected to time-lapse fluorescence wound healing assay. We also studied the effect of MET inhibition, chemotherapy, and their combined impact on breast cancer motility. Time-lapse images were subjected to commercial packages for segmentation and tracking - allowing us to extract morphokinetic (MK) information at a single-cell resolution. To better understand cell motility patterns, we developed the Tool for Analysis of Single-Cell (TASC) infrastructure, which utilizes unsupervised machine learning methods to demonstrate a high-dimensional feature set at a single-cell resolution. We compared our experimental MK results to simulations of three modified classical physical models: Lévy flight (LF), fractal Brownian motion, and random walk (RW) with or without a back-propagating wave (BPW). TASC analysis demonstrated a fundamental difference between ER-positive and TNBC cells: both cell types contained two subpopulations harboring similar MK characteristics. TNBC cells presented an additional subpopulation characterized by 1) dominantly increasing cumulative kinetics values (mean square displacement - MSD), 2) highest temporal wave values (velocity starting-time), and 3) a decrease in non-cumulative kinetics values. The combined treatment of TNBC cells with MET inhibition and chemotherapy eliminates this high-motility group. Cell motility models and motion simulations were compared using Wasserstein's analysis. Untreated TNBC cells showed high similarities to RW with BPW simulations, while MET-activated TNBC cells transformed into LF without BPW simulations, indicating more aggressive behavior. TASC analysis revealed a previously undefined high motility subpopulation of TNBC that was eradicated by combining MET inhibition and chemotherapy. The primary benefit of TASC is the ability to cluster and characterize similar behaviors across different motility models while still detecting subtle changes within each one. MK analysis combined with TASC can lead to discovering novel targets for precise therapy. Moreover, this infrastructure may determine patient tumor susceptibility to chemotherapy and biological treatments and operate as an analytical tool for personalized, targeted therapy. Citation Format: Ilan Tsarfaty, Or Megides, Ohad Doron, Hagar Alaloof, Ori Moskowitz, Judith Horev. Morphokinetic single-cell analysis and machine learning as a tool to characterize breast cancer cell motility and response to therapy [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-089.

ANALYSIS OF THE APPLICABILITY OF PHYSICAL MODELS TO DESCRIBE DENSIFICATION OF LITHIUM FERRITE COMPACTS DURING SINTERING IN THE FIELD OF INTENSE ELECTRON BEAM

The paper analyzes the possibility of describing the accelerated sintering of lithium-titanium compacts under high-power radiation effects in terms of classical physical models developed by Kuczynski and Johnson based on the kinetic analysis of ferrite compact densification. LiTi ferrites synthesized by ceramic technology in laboratory conditions were investigated. The first portion of the compacts was sintered in air in thermal ovens at 900–1100°C for 30 minutes at a heating rate of 900 °C/min. The second portion of the compacts was sintered in a similar mode but using a radiation-thermal method: the exposure to pulsed electron beam with energy of 1.5–2.0 MeV using the ILU-6 accelerator. The geometrical dimensions of the compacts were measured before and after sintering to determine shrinkage. Mathematical approximation of the experimental shrinkage data showed the discrepancy of both models to describe sintering of LiTi ferrite samples both under thermal sintering conditions and radiationthermal effect.

The Triangle Wave Versus the Cosine: How Classical Systems Can Optimally Approximate EPR-B Correlations.

The famous singlet correlations of a composite quantum system consisting of two two-level components in the singlet state exhibit notable features of two kinds. One kind are striking certainty relations: perfect anti-correlation, and perfect correlation, under certain joint settings. The other kind are a number of symmetries, namely invariance under a common rotation of the settings, invariance under exchange of components, and invariance under exchange of both measurement outcomes. One might like to restrict attention to rotations in the plane since those are the ones most commonly investigated experimentally. One can then also further distinguish between the case of discrete rotations (e.g., only settings which are a whole number of degrees are allowed) and continuous rotations. We study the class of classical correlation functions, i.e., generated by classical physical systems, satisfying all these symmetries, in the continuous, planar, case. We call such correlation functions classical EPR-B correlations. It turns out that if the certainty relations and rotational symmetry holds at the level of the correlations, then rotational symmetry can be imposed “for free” on the underlying classical physical model by adding an extra randomisation level. The other binary symmetries are obtained “for free”. This leads to a simple heuristic description of all possible classical EPR-B correlations in terms of a “spinning bi-coloured disk” model. We deliberately use the word “heuristic” because technical mathematical problems remain wide open concerning the transition from finite or discrete to continuous. The main purpose of this paper is to bring this situation to the attention of the mathematical community. We do show that the widespread idea that “quantum correlations are more extreme than classical physics would allow” is at best highly inaccurate, through giving a concrete example of a classical correlation which satisfies all the symmetries and all the certainty relations and which exceeds the quantum correlations over a whole range of settings. It is found by a search procedure in which we randomly generate classical physical models and, for each generated model, evaluate its properties in a further Monte-Carlo simulation of the model itself.

Characteristics of heat flow as well as process of heat conduction and transport in partitioned thermal convection

How to improve the heat transfer efficiency of the system is always a hot issue in thermal convection research field. It is found that when the partitions are added at equal distances to the classical physical model -- Rayleigh-Bénard convection system and gaps are left between the horizontal plates and partitions, the heat transfer efficiency of the system increases significantly with the number of partitions increasing. The heat transfer efficiency can reach up to 3.1 times that of the non-partition device with the specific geometric parameters. In this paper, the Direct Numerical Simulation (DNS) method is used to simulate the partitioned convection system. The mechanism of the heat transfer enhancement of the system is analyzed by studying the characteristics of the heat flow as well as the heat conduction and transport in the system. After the flow in partitioned convection system is fully developed, the fluid in each channel moves vertically in alternating direction and the upward(downward) channel has a higher(lower) temperature than the average temperature of the cell. Due to the symmetry of the system, only the bottom region of the low temperature channel, the bottom region of the high temperature channel and the gap region connecting these two channels are selected for research. By discussing the lateral and longitudinal transport processes of heat flow in the above three regions, the heat flux in the channels and gap areas of the system are studied by quantitative analysis. The results show that the low-temperature fluid in the channel impacts on the bottom plate and then moves into the gap area; the fluid is continuously heated by the bottom plate and flows out of gap area with high temperature. Finally, the fluid converges in the heat transfer channel and forms a longitudinal jet. The external input heat flux of the area that has only horizontal heat transport is large, accounting for 92% of the total heat flux which is obtained from the bottom plate. The heat flux of gap area accounts for 64% of the total heat flux, but the external input heat flux of the area that has only longitudinal heat transport is smaller. The convection system with different geometric parameters has the effect of enhancing heat transfer efficiency, and enhancement capability of the system is stronger when the height of gaps is smaller. When the number of partitions <i>n</i> = 11 and the height of gap <i>d</i> = 0.01, the value of the global <i>Nu</i> number is largest, <i>Nu</i> = 82, which is more than three times that of the system without partitions.

The quantitative characterization of the ignition process for a lean staged injector: Influence of the air split between pilot swirlers

The ignition of a lean staged injector aimed at aeronautical application is a transient and complex phenomenon, which involves fluid dynamics, turbulent mixing, chemical kinetics, as well as their mutual interactions. In the present research, a staged injector, designed based on stratified partially premixed combustion concept, is introduced. The ignition performance of stratified partially premixed injectors with different air split ratios between pilot swirlers are experimentally acquired, which exhibits apparent distinctions. In order to make quantitative analyses, the classical physical ignition model is improved, in which the flame propagation process is further divided into the axial and radial propagation sub-processes. Nonreacting flow field and discrete phase simulations, validated by experiment results, are utilized to obtain the velocity and spray distributions. Physical parameters characterizing the ignition sub-processes are defined and calculated based on the numerical simulation results. Conclusions are made by comparing the physical parameters of the ignition sub-processes. The radial propagation of the ignition kernel is responsible for the ignition performance difference between the two injectors with different pilot air split ratios (PASR) in that the average equivalence ratio along the radial propagation route of PASR = 7:3 is one order richer than that of PASR = 2:8. The present ignition analysis and model can be further extended and developed for the optimization of ignition performance of lean staged injector.

Distributed Modeling Of Building Systems Through Cyber-Physical Integration

This article describes the proposed approaches to creating distributed models that can, with given accuracy under given restrictions, replace classical physical models for construction objects. The ability to implement the proposed approaches is a consequence of the cyber-physical integration of building systems. The principles of forming the data structure of designed objects and distributed models, which make it possible to uniquely identify the elements and increase the level of detail of such a model, are presented. The data structure diagram of distributed modeling includes, among other things, the level of formation and transmission of signals about physical processes inside cyber-physical building systems. An enlarged algorithm for creating the structure of the distributed model which describes the process of developing a data structure, formalizing requirements for the parameters of a design object and its operating modes (including normal operating conditions and extreme conditions, including natural disasters) and selecting objects for a complete group that provides distributed modeling is presented. The article formulates the main approaches to the implementation of an important practical application of the cyber-physical integration of building systems - the possibility of forming distributed physical models of designed construction objects and the directions of further research are outlined.

An Effective Algorithm for Single Image Fog Removal

Poor visibility due to the effects of light absorption and scattering is challenging for processing images captured in foggy weather conditions. This paper proposes an effective algorithm for single image fog removal based on degradation model and group-based sparse representation (GSR). The proposed degradation model is constructed based on a classical physical model, i.e., dichromatic atmospheric scattering model. Then, the new degradation model is integrated into the group-based sparse representation framework. Finally, the single image defogging problem is regarded as an image restoration problem, which can be well optimized by GSR. The method is compared with several well-known algorithms from the literature using qualitative and quantitative evaluations, and results indicate considerable improvement over existing algorithms.

Theoretical design and regulation of low-dimensional spintronics

Spintronics, using spins of electrons for the information transmission, process and storage, which is a promising technology in the future. Low-dimensional materials, with strong quantum effect, is an ideal platform for the study of spin-charge interactions and could be utilized as spintronics devices. So far, due to the excess freedoms provided by defect, impurity, interface and boundary effects, the magnetic systems with long-range order are difficult to obtain and manipulate. Exploring novel materials in low-dimensional remains a big challenge. In recent years, based on the well-developed first-principle calculations, together with the classical physical model, its efficient to predict the critical phenomena in low-dimensional materials accurately, which can provide the insights into the spintronics. Moreover, the hybrid and more advanced calculation methods are utilized as effective to understand the electronic structures of strongly correlated systems with consideration of the coulomb interaction between the local d or f electrons. Plus, the magnetic anisotropic energy is also calculated to confirm the easy-magnetization axis, which could be useful to determine the moderate interaction models and further to anticipate the critical behavior in ferromagnetic or antiferromagnetic systems. To utilize the low-dimensional spintronics above room-temperature, high critical transition temperature is prerequisite and in other words, the coupling mechanism matters. Here, through the overview of the recent low-dimensional researches, the highly reliable low-dimensional spintronics are summarized and their exchange interactions are discussed. For one-dimensional spintronics, ligand-H saturated 3d metal chains and MOF-like organic systems are introduced. The mechanical and thermodynamic stabilities are validated and electronic properties shows that the ground states are ferromagnetic or antiferromagnetic, which both own the high magnetization anisotropic energy. Unlike the benzene-ligand ferromagnetic chains, hydrogen can bring the direct interaction into one-dimensional chains and further modulate the transmission properties at the same time. The direct exchange interaction can be a better way to remain the magnetic states. Another MOF-like chains, the exchange interactions are through the basal plane of carbon materials, which could be a super-long exchange interaction. For two-dimensional materials, the CrI3 and Cr2Ge2Te6 are narrowed down to monolayer successfully and they are both ferromagnetic, which the critical transition behaviors can be reproduced using Ising or XY model. Besides, the high throughput calculations are carried out to screen the potential two-dimensional spintronics. However, the understanding of the exchange interactions which is important for keeping the ferromagnetic or antiferromagnetic states is rare. In the Fe-Si alloy systems, a possible two-dimensional system is designed and the ground states is ferromagnetic. Additionally, it is half-metallic and the curie temperature can reach up to 780 K, which is a promising candidate to investigate the two-dimensional spintronics and potential is applications. Other recent calculations also show that through the W-dopant in CrI3 or Cr2Ge2Te6, the curie temperature can be improved, which originates from the narrowing of virtual gaps between orbitals and enhances the coupling interactions. At last, the modulation of spintronics through magnetic fields is difficult for the device miniaturization. Hence, using the electronic field is more convenient and a novel spintronics called bipolar magnetic semiconductor is introduced as a potential material for spintronics. Some other methods like strain and carrier doping could also be utilized to regulate the intrinsic magnetic behaviors reversely.

DeeptransMap: a considerably deep transmission estimation network for single image dehazing

Due to the ill-posed phenomenon of the classical physical model, single image dehazing based on the model has been a challenging vision task. In recent years, applying machine learning techniques to estimate a critical parameter transmission has proven to be an effective solution to this issue. Accordingly, the robustness and accuracy of learning-based transmission estimation model is extremely important, since it does impact on the final dehazing effects. The state-of-the-art dehazing algorithms by this means generally use haze-relevant features as the single input to their transmission estimation models. However, the used haze-relevant features sometimes are not sufficient and reliable in holding real intrinsic information related to haze due to their two shortcomings and ultimately bring about their less effectiveness for some dehazing cases. Based on related efforts on representation learning and deep convolutional neural networks, in this paper, we seek to achieve the robustness and accuracy of transmission estimation model for bolstering the effectiveness of single image dehazing. Specifically, we propose a hybrid model combining unsupervised and supervised learning in a considerably deep neural networks architecture, termed DeeptransMap, in order to achieve accurate transmission map from a single image. Experimental results demonstrate that our work performs favorably against several state-of-the-art dehazing methods with the same estimated goal and keeps efficient in terms of the computational complexity of transmission estimation.

Interactions between Ehrenfest’s urns arising from group actions

The Ehrenfest diffusion model is a well-known classical physical model consisting of two urns and n balls. There is a group theoretical interpretation of the model by using the Gelfand pair $$({\mathbb {Z}}/2{\mathbb {Z}}\wr S_{n},S_{n})$$ by Diaconis and Shahshahani (Z Wahrsch Verw Gebiete 57(2):159–179, 1981). This interpretation is still valid for an r-urns generalization. Then the corresponding Gelfand pair is $$(S_{r}\wr S_{n},S_{r-1}\wr S_{n})$$ . However, in these models, there are no restrictions for ball movements, i.e., each ball can freely move to any urns. In this paper, interactions between urns arising from actions of finite groups are introduced. Degree of freedom of ball movements are restricted by finite group actions. We then show that the cutoff phenomenon occurs in some particular (yet significant and interesting) cases.

Interference Model Similarity Index and Its Applications to Millimeter-Wave Networks

In wireless communication networks, interference models are routinely used for tasks, such as performance analysis, optimization, and protocol design. These tasks are heavily affected by the accuracy and tractability of the interference models. Yet, quantifying the accuracy of these models remains a major challenge. In this paper, we propose a new index for assessing the accuracy of any interference model under any network scenario. Specifically, it is based on a new index that quantifies the ability of any interference model in correctly predicting harmful interference events, that is, link outages. We consider specific wireless scenario of both conventional sub-6 GHz and millimeter-wave networks and demonstrate how our index yields insights into the possibility of simplifying the set of dominant interferers, replacing a Nakagami or Rayleigh random fading by an equivalent deterministic channel, and ignoring antenna sidelobes. Our analysis reveals that in highly directional antenna settings with obstructions, even simple interference models (such as the classical protocol model) are accurate, while with omnidirectional antennas, more sophisticated and complex interference models (such as the classical physical model) are necessary. Our new approach makes it possible to adopt the simplest interference model of adequate accuracy for every wireless network.