Physical Foundations Research Articles

Moderate Physical Perspectivalism

Abstract Recent developments in foundations of physics have given rise to a class of views suggesting that physically meaningful descriptions must always be relativized to a physical perspective. In this article I distinguish between strong physical perspectivalism, which maintains that all facts must be relativized to a perspective, and moderate physical perspectivalism, which maintains that all empirically meaningful descriptions must be relativized to a perspective. I argue that scientific evidence and philosophical considerations support moderate physical perspectivalism over strong physical perspectivalism. In particular, motivations connected to epistemic humility and the social nature of science are more compatible with the moderate approach.

Peeking into the future: inferring mechanics in dynamical tissues.

Cells exert forces on each other and their environment, shaping the tissue. The resulting mechanical stresses can be determined experimentally or estimated computationally using stress inference methods. Over the years, mechanical stress inference has become a non-invasive, low-cost computational method for estimating the relative intercellular stresses and intracellular pressures of tissues. This mini-review introduces and compares the static and dynamic modalities of stress inference, considering their advantages and limitations. To date, most software has focused on static inference, which requires only a single microscopy image as input. Although applicable in quasi-equilibrium states, this approach neglects the influence that cell rearrangements might have on the inference. In contrast, dynamic stress inference relies on a time series of microscopy images to estimate stresses and pressures. Here, we discuss both static and dynamic mechanical stress inference in terms of their physical, mathematical, and computational foundations and then outline what we believe are promising avenues for in silico inference of the mechanical states of tissues.

Contribution of interference to the magneto-optical transverse Kerr effect in white light

Objectives. When measuring the transverse Kerr effect on thin-film structures, interference effects have a great influence on the result obtained. In conference presentations, some researchers have reported on the use of white light in experiments. In their opinion, despite the thickness of the studied layers being much less than the wavelength of light, white light can help avoid interference effects and/or resonant excitation of plasmon waves. The aim of the present work is to verify the validity of such statements using simulation.Methods. In order to solve this problem, the method of computer simulation was used. A numerical solution of equations was compiled for a model structure for various thicknesses and materials of layers.Results. The simulation results show that interference effects in different parts of the spectrum when using white light sources do not neutralize each other. The magnitude of the effect is affected not only by the thickness of the structure layers, but also by the shape of the source emission spectrum, as well as the sensitivity curve of the photodetector. In this case, the output of the measured value of the effect to a plateau at relatively large thicknesses of the magnetooptical film is due to the light being absorbed in the thickness of the magneto-optical film and is negligibility of the back reflection of light from the substrate.Conclusions. The presented technique takes into account the influence of interference effects when measuring the equatorial Kerr effect in white light or using other sources having a wide spectral range, thus improving the interpretation of experimental results. The results are relevant to the development and research of the physical foundations for creating new and improving existing devices in micro-, nano-, and solid-state electronics, as well as quantum devices, including optoelectronic devices and converters of physical quantities.

Permafrost instability negates the positive impact of warming temperatures on boreal radial growth

Climate warming can alleviate temperature and nutrient constraints on tree growth in boreal regions, potentially enhancing boreal productivity. However, in permafrost environments, warming also disrupts the physical foundation on which trees grow, leading to leaning trees or "drunken" forests. Tree leaning might reduce radial growth, undermining potential benefits of warming. Here, we found widespread radial growth reductions in southern latitude boreal forests since the 1980s. At mid latitudes, radial growth increased from ~1980 to ~2000 but showed recent signs of decline afterward. Increased growth was evident since the 1980 s at higher latitudes, where radial growth appears to be temperature limited. However, recent changes in permafrost stability, and the associated increased frequency of tree leaning events, emerged as a significant stressor, leading to reduced radial growth in boreal trees at the highest latitudes, where permafrost is extensive. We showed that trees growing in unstable permafrost sites allocated more nonstructural carbohydrate reserves to offset leaning which compromised radial growth and potential carbon uptake benefits of warming. This higher allocation of resources in drunken trees is needed to build the high-density reaction wood, rich in lignin, that is required to maintain a vertical position. With continued climate warming, we anticipate widespread reductions in radial growth in boreal forests, leading to lower carbon sequestration. These findings enhance our understanding of how climate warming and indirect effects, such as ground instability caused by warming permafrost, will affect boreal forest productivity in the future.

Magnetic properties of tire cord as a diagnostic parameter of car tires condition

This article presents the possibility of using the magnetic measurement method, which is an alternative method to shearography as a method of testing the condition of the cord of all-steel tires in order to determine the possibility of using the casing for the retreading process. It is an extension of the design and research work aimed at creating a prototype of a diagnostic device. As part of the activities, a prototype of the device and a version of the measuring head were developed, but a prototype of the algorithm of the measurement program was also created, which uses the physical foundations of non-destructive measurements of motor vehicle tires developed within the project. This study is related to the increasingly strict regulations and the intentions of the European Commission regarding the use of vehicles, which require an increase in the share of recycled materials, including tires. Therefore, there is a need to develop measurement methods that allow for the assessment of the quality of the casing while the used tires are still stored. This work presents the results of theoretical analyses of the possibility of using the magnetic field as a diagnostic parameter of the condition of tires

Land surface scheme TerM: the model formulation, code architecture and applications

Abstract This paper presents the INM RAS–MSU land surface scheme, extracted from the INM RAS Earth system model into an independent software complex and supplemented with several modules to reproduce new components and processes of the Earth system. The resulting software product is referred to as TerM (Terrestrial Model). The physical and mathematical foundations of the model, the main features of the software implementation, and examples of applications in reproducing components of the terrestrial hydrological and carbon cycles are briefly outlined. Separating the land surface block into a standalone software complex significantly saves computational resources when assessing the impact of global and regional climate changes on natural resources (including hydrological ones), ecosystem dynamics, and emissions of climate-relevant substances with high spatial detalization. Within the TerM modelling complex, the development, validation, and calibration of new parameterizations of physical and biogeochemical processes are being conducted in an autonomous mode for subsequent implementation into the full INM RAS Earth system model.

Physics-enhanced data-driven turbulence model for flow around submerged bodies

Data-driven turbulence modeling is regarded as an effective approach to enhance the predictive performance of Reynolds-Averaged Navier−Stokes (RANS) models. However, the stability and accuracy of such methods still require further improvement. Therefore, we propose a novel modified model based on a combined neural network. Within this framework, we construct a fully connected neural network to predict the eddy viscosity coefficient in the RANS equations, thereby achieving an implicit solution for the linear part of the Reynolds stress. Additionally, we introduce a tensor basis neural network to predict the higher-order eddy viscosity relationships between unclosed quantities and analytical quantities. Furthermore, the model incorporates pressure gradients and turbulent kinetic energy gradients as inputs, fully considering the influence of pressure gradients and strong non-equilibrium effects in turbulence, thereby enhancing the physical foundation of the model. To evaluate the performance of the constructed model in different-dimensional flow fields with higher Reynolds numbers, we conduct validation analyzes on a two-dimensional NACA0012 model and a three-dimensional SUBOFF model without appendages. The results indicate that the modified model consistently outperforms the RANS model, particularly in predicting the velocity field near the leading edge of the NACA0012 hydrofoil. The predictions of the modified model are closer to those of large eddy simulation (LES) results. Specifically, the root mean square error (RMSE) of the interpolated and extrapolated modified models is reduced by 49.2% and 33.3%, respectively, compared to the RMSE of the RANS model. When applied to three-dimensional isolated SUBOFF flows, the modified model’s predictions for the average velocity field and pressure coefficients at both ends of the SUBOFF are in high agreement with LES results, but the prediction accuracy in the middle section is slightly insufficient. Nevertheless, the prediction errors of the interpolated and extrapolated modified models are reduced by 69.5% and 63.6%, respectively, compared to the baseline RANS model, which fully demonstrates the high accuracy of the modified model in predicting the overall pressure distribution of the SUBOFF. The methods developed in this study provide valuable insights into high-precision intelligent modeling for two-dimensional and three-dimensional flows.

Magnetic domain walls interacting with dislocations in micromagnetic simulations

Defects, impurities, and embedded particles in ferromagnetic materials are long known to be responsible for the Barkhausen effect due to the jerky field-driven motion of domain walls and have more recently been shown to play a role also in domain wall dynamics in nanoscale ferromagnetic structures used in spintronics devices. Simulating the magnetic domain wall dynamics in the micromagnetic framework offers a straightforward route to study such systems and phenomena. However, the related work in the past suffers from material imperfections being introduced without proper physical foundation. Here, we implement dislocation stress fields in micromagnetic simulations through the induced anisotropy fields by inverse magnetostriction. The effects of individual dislocations on domain wall dynamics in thin films of different Fe surface lattice planes are characterized numerically. As a demonstration of the applicability of the implementation, we consider disorder fields due to randomly positioned dislocations with different densities, and study the avalanche-like transient approach towards the depinning transition of a domain wall driven by a slowly increasing external magnetic field.

Data Quality in the Fitting of Approximate Models: A Computational Chemistry Perspective.

Empirical parametrization underpins many scientific methodologies including certain quantum-chemistry protocols [e.g., density functional theory (DFT), machine-learning (ML) models]. In some cases, the fitting requires a large amount of data, necessitating the use of data obtained using low-cost, and thus low-quality, means. Here we examine the effect of using low-quality data on the resulting method in the context of DFT methods. We use multiple G2/97 data sets of different qualities to fit the DFT-type methods. Encouragingly, this fitting can tolerate a relatively large proportion of low-quality fitting data, which may be attributed to the physical foundations of the DFT models and the use of a modest number of parameters. Further examination using "ML-quality" data shows that adding a large amount of low-quality data to a small number of high-quality ones may not offer tangible benefits. On the other hand, when the high-quality data is limited in scope, diversification by a modest amount of low-quality data improves the performance. Quantitatively, for parametrizing DFT (and perhaps also quantum-chemistry ML models), caution should be taken when more than 50% of the fitting set contains questionable data, and that the average error of the full set is more than 20 kJ mol-1. One may also follow the recently proposed transferability principles to ensure diversity in the fitting set.

Particle Identification in High Energy Collisions at RHIC

This article provides a technical introduction to the study of collider physics by focusing on the concept of particle identification (PID). Through a general overview of the Relativistic Heavy Ion Collider (RHIC) and the Pioneering High Energy Nuclear Interaction Experiment (PHENIX), the author discusses the role of Vanderbilt University researchers in collaborative work at the Brookhaven National Laboratory. After explaining the concept of event reconstruction and centrality with graphical images of experimental results, the author outlines the time-of-flight method of particle identification in high energy physics. A final presentation of the design concept for the Multi-Gap Resistive Plate Chamber (MRPC) integrates the more traditional foundations of theoretical physics with the next generation of physics experimentation in the field.

NEW LABORATORY WORK, VISUAL DEMONSTRATIONS, AND THE USE OF PROBLEMS AND EXERCISES TO STUDY THE PHYSICAL FOUNDATIONS OF ENERGY-SAVING TECHNOLOGIES

This article analyzes the importance of laboratory work, visual aids, and problem-solving in studying the physical foundations of energy-saving technologies. The research findings show that laboratory exercises and practical models help students develop practical skills and deepen their knowledge of energy efficiency. Additionally, through problems and exercises, students gain the opportunity to understand practical methods of energy-saving.

The limits of mathematics in physics

Mathematics is considered the language of physics. Starting from idealizations and kinematics, geometric-mathematical physics with paradigms such as flexible spacetime and dark energy has emerged whose physical reality has not been clarified. By analyzing processes regarding their causes and the functional dependencies of energies, this work identifies shortcomings in the basic energy concepts of physics, which cannot be remedied with mathematics. While formal transformations of process equations such as integration are mathematically correct, they do conceal vital physical information, suggesting that mathematics should be used with caution. We propose a physically justified approach that reconciles the mechanical and thermodynamic energy concepts and provides a revised interpretation of E = mc2. Our results suggest that geometric approaches were built too early on a shaky physical foundation, leading to undesirable developments in recent centuries. The language of physics should be physics, not just mathematics.

An evaluation of the hybrid Fokker–Planck-DSMC approach for high-speed rarefied gas flows

The Direct Simulation Monte Carlo (DSMC) method has been widely used for simulations of rarefied gas flows. It has been proven that the numerical solution of the DSMC method converges to the Boltzmann equation, providing a solid physical foundation for high Knudsen numbers. However, many aerospace engineering problems include both low and high-density regions in a single domain, making the DSMC method computationally expensive. Over the past decade, the particle Fokker–Planck (FP) method has been studied as a means to reduce computational costs near the continuum regime. While enhancing efficiency, the FP method loses physical accuracy at high Knudsen numbers. Aiming for universal accuracy and efficiency across the whole range of Knudsen numbers, the hybrid FP-DSMC method has been studied. Nevertheless, consistent comparisons among the DSMC, FP, and hybrid FP-DSMC methods have received limited attention so far. This paper presents a consistent comparative study of the DSMC, FP, and hybrid FP-DSMC methods to assess the accuracy and efficiency of the hybrid FP-DSMC method. The hybrid FP-DSMC solver is developed based on the open-source SPARTA framework. The benchmark problems include supersonic flow in a planar nozzle, hypersonic flow around a cylinder, and hypersonic flow around a THAAD-like missile. The results demonstrate that the hybrid FP-DSMC method can be more efficient than the DSMC method while being more accurate than the FP method. The speed-up achieved by the FP-DSMC method ranged from 1.5 to 14 times compared to the converged DSMC simulation.

Instantaneous and Retarded Interactions in Coherent Radiation.

In coherent radiation of an ensemble of electrons, the radiation field from electrons resonantly drives the other electrons inside to produce stimulated emission. The radiation reaction force on the electrons accounting for this stimulated radiation loss is classically described by the Liénard-Wiechert potential. Despite its being the foundation of beam physics for decades, we show that using the "acceleration field" in Liénard-Wiechert potential to describe radiative interactions leads to divergences due to its implicit dependence on instantaneous interactions. Here, we propose an alternative theory for electromagnetic radiation by decomposing the interactions into an instantaneous part and retarded part. It is shown that only the retarded part contributes to the irreversible radiation loss and the instantaneous part describes the space charge related effects. We further apply this theory to study the coherent synchrotron radiation energy loss, which hopefully will reshape our understanding of coherent radiation and collective interactions.

Effect of salinity-clay variation on the transient magnetic field in the Quaternary aquifer, theoretically and practically

This study focuses on understanding the physical foundations of the transient electromagnetic method, such as the transition from a simple basic principle to a more complex principle, starting from the Biot-Savart law until the current concept. It calculates the steady and transient magnetic fields around any electrical wire system. Accordingly, this law will be used to determine the magnetic field around the square loop configuration, which matches the magnetic field arising from the circular loop configuration. The square loop equation was derived and extended to include changes in the half-space, which allows us to understand how the electromagnetic waves travel in the subsurface and the farthest point, that can record the TEM response from the TEM loop, according to the loop size and loop current. Accordingly, the deduced equations were applied to predict the transient magnetic field curves of the Quaternary aquifer in three different water zones along the Nile delta, depending on the values of resistivity and chargeability caused by the variations in salinity and clay contents. The calculated transient magnetic curves were then compared with other measured field curves to confirm the validity of the derived equations. Therefore, these curves are expected to be used as guide curves for the transient magnetic field response in this aquifer. Also, these curves are important in estimating the hydro-geophysical characteristics of the main groundwater aquifer in the selected area and in other areas with the same geologic and hydrogeologic settings. Also, a common model is presented for any delta environment in the world in measured and calculated data.

Creating and controlling global Greenberger-Horne-Zeilinger entanglement on quantum processors

Greenberger-Horne-Zeilinger (GHZ) states, also known as two-component Schrödinger cats, play vital roles in the foundation of quantum physics and the potential quantum applications. Enlargement in size and coherent control of GHZ states are both crucial for harnessing entanglement in advanced computational tasks with practical advantages, which unfortunately pose tremendous challenges as GHZ states are vulnerable to noise. Here we propose a general strategy for creating, preserving, and manipulating large-scale GHZ entanglement, and demonstrate a series of experiments underlined by high-fidelity digital quantum circuits. For initialization, we employ a scalable protocol to create genuinely entangled GHZ states with up to 60 qubits, almost doubling the previous size record. For protection, we take a different perspective on discrete time crystals (DTCs), originally for exploring exotic nonequilibrium quantum matters, and embed a GHZ state into the eigenstates of a tailor-made cat scar DTC to extend its lifetime. For manipulation, we switch the DTC eigenstates with in-situ quantum gates to modify the effectiveness of the GHZ protection. Our findings establish a viable path towards coherent operations on large-scale entanglement, and further highlight superconducting processors as a promising platform to explore nonequilibrium quantum matters and emerging applications.

General Definitions of Information, Intelligence, and Consciousness from the Perspective of Generalized Natural Computing

Atoms themselves have no thoughts and cannot be thinking. Why does the human body, which is composed of atoms, have consciousness? The widely used concepts of information and intelligence in today’s science, which are related to this, do not yet have appropriate general definitions. Answering these interesting questions is a crucial issue for technological development in the historical context of human society entering the era of intelligence. The key lies in how to fully utilize the existing fundamental theories subtly related to information science. Here we attempt to give the definition of general information and general intelligence from the perspective of generalized natural computing, based on the least action principle, Hamilton-Jacobi equation, dynamic programming, reinforcement learning, and point out the relationship between the two. The least action principle for describing conservative systems can be seen as an intelligent manifestation of natural matter, and its equivalent form, the Hamilton-Jacobi equation, can be extended to describe quantum phenomena and is a special case of continuous dynamic programming equations. Dynamic programming is an efficient optimization method under deterministic models, while reinforcement learning, as a manifestation of biological intelligence, is its model-free version. The statement that reinforcement learning is the most promising machine learning method has a profound physical foundation. General information is defined as the degree to which a certain environmental element determines the behavior of the subject. General intelligence is defined as the automatic optimization ability of the action or value function of a system with a certain degree of conservatism. Intelligence is a basic property of material systems, rather than an emergent property that only complex systems possess. Consciousness is an advanced intelligent phenomenon, a reconstruction of quasi conservative systems based on complex systems.

Robust finite-temperature many-body scarring on a quantum computer

Mechanisms for suppressing thermalization in disorder-free many-body systems, such as Hilbert space fragmentation and quantum many-body scars, have recently attracted much interest in foundations of quantum statistical physics and potential quantum information processing applications. However, their sensitivity to realistic effects such as finite temperature remains largely unexplored. Here, we have utilized IBM's Kolkata quantum processor to demonstrate an unexpected robustness of quantum many-body scars at finite temperatures when the system is prepared in a thermal Gibbs ensemble. We identify such robustness in the PXP model, which describes quantum many-body scars in experimental systems of Rydberg atom arrays and ultracold atoms in tilted Bose-Hubbard optical lattices. By contrast, other theoretical models which host exact quantum many-body scars are found to lack such robustness and their scarring properties quickly decay with temperature. Our study sheds light on the important differences between scarred models in terms of their algebraic structures, which impacts their resilience to finite temperature. Published by the American Physical Society 2024

Scientific thinking across the centuries and the foundations of physics

Abstract This paper discusses the sense in which physics can be seen, contrary to what is often stated, as a cumulative body of knowledge on Nature. Science, from this perspective, is the search for increasingly effective ways of organizing our experience, hence giving us the best available grasp on reality. The paper illustrates the continuity of science across the centuries by comparing the physics of motion of Aristotle and Newton.

Sediment Corrections for Distributed Acoustic Sensing

AbstractOn continental margins, sediments cause significant and spatially variable delays in seismic phase arrival times. The strong impedance contrast of the sediment‐bedrock interface causes P‐wave splitting that is clearly seen on distributed acoustic sensing recordings of earthquakes, resulting in additional phase arrivals that must be picked separately. We introduce sediment corrections to correctly account for those additional phases in the hypocenter localization procedure. Conceptually, the sediment correction method differs from the commonly‐used station corrections; instead of introducing a mathematically optimal constant time delay for each station and each phase, the corrections are derived from a physical, first‐order modeling of the wave propagation in the sediments. To calibrate the sediment corrections, a two‐step procedure is adopted: (a) the delay between the P‐phase and the converted Ps‐phase is taken as a proxy of the sediment thickness; (b) the P‐ and S‐wave speeds are determined through inversion. We show that sediment corrections are able to account for most of the observed bias while considerably reducing the number of free parameters compared to classical station correction. Moreover, the observed local delays are almost fully explained by the presence of the sedimentary layer, rather than by the 3D velocity variations of the bedrock. We retrieve and values that are compatible with values commonly found for sediments. Given the simplicity and physical foundation of the proposed method, we recommend the use of sediment corrections over station corrections whenever significant P‐wave splitting can be observed.