Mass-Oscillators as Information Memories of Action

In the theory of physical information, the physical phenomena of electromagnetism, quantum mechanics and gravity can be described by means of the action as information enclosed in four dimensional structures with oscillator properties, under the conditions of the Hamilton principle. The present report shows that it is also possible to simulate the behaviour of the mass under these conditions. As a result, among other things, the statements are obtained that the mass is stored virtual action;the rest frame of elementary objects and the inertia of matter are caused by the action stored in the mass oscillators.

Transient Thermal Distribution in a Wavy Fin Using Finite Difference Approximation Based Physics Informed Neural Network

Heat transport has been significantly enhanced by the widespread usage of extended surfaces in various engi-neering domains.Gas turbine blade cooling,refrigeration,and electronic equipment cooling are a few prevalent applications.Thus,the thermal analysis of extended surfaces has been the subject of a significant assessment by researchers.Motivated by this,the present study describes the unsteady thermal dispersal phenomena in a wavy fin with the presence of convection heat transmission.This analysis also emphasizes a novel mathematical model in accordance with transient thermal change in a wavy profiled fin resulting from convection using the finite difference method(FDM)and physics informed neural network(PINN).The time and space-dependent governing partial differential equation(PDE)for the suggested heat problem has been translated into a dimensionless form using the relevant dimensionless terms.The graph depicts the effect of thermal parameters on the fin’s thermal profile.The temperature dispersion in the fin decreases as the dimensionless convection-conduction variable rises.The heat dispersion in the fin is decreased by increasing the aspect ratio,whereas the reverse behavior is seen with the time change.Furthermore,FDM-PINN results are validated against the outcomes of the FDM.

基于物理信息引导深度学习的建筑响应实时预测方法

High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures,including post-earthquake damage assessment,structural health monitoring,and seismic resilience assessment of buildings.To improve the accuracy and efficiency of structural response prediction,this study proposes a novel physics-informed deep-learning-based realtime structural response prediction method that can predict a large number of nodes in a structure through a data-driven training method and an autoregressive training strategy.The proposed method includes a Phy-Seisformer model that incorporates the physical information of the structure into the model,thereby enabling higher-precision predictions.Experiments were conducted on a four-story masonry structure,an eleven-story reinforced concrete irregular structure,and a twenty-one-story reinforced concrete frame structure to verify the accuracy and efficiency of the proposed method.In addition,the effectiveness of the structure in the Phy-Seisformer model was verified using an ablation study.Furthermore,by conducting a comparative experiment,the impact of the range of seismic wave amplitudes on the prediction accuracy was studied.The experimental results show that the method proposed in this paper can achieve very high accuracy and at least 5000 times faster calculation speed than finite element calculations for different types of building structures.

Building Blocks of Nature

By means of a representation of the elementary objects by the Lagrange density and by the commutators of the communication relations, correlations can be formed using the Fourier transform, which under the conditions of the Hamilton principle, describes correlation structures of the elementary objects with oscillator properties. The correlation structures obtained in this way are characterized by physical information, the essential component of which is the action. The correlation structures describe the physical properties and their interactions under the sole condition of the Hamilton’s principle. The structure, the properties and the interactions of elementary objects can be led back in this way to a fundamental four dimensional structure, which is therefore in their different modifications the building block of nature. With the presented method, an alternative interpretation of elementary physical effects to quantum mechanics is obtained. This report provides an overview of the fundamentals and statements of physical information theory and its consequences for understanding the nature of elementary objects.

A New Paradigm for the Extraction of Information: Application to Enhancement of Visual Information in a Medical Application

The noninvasive evaluation of the cardiac function presents a great interest for the diagnosis of cardiovascular diseases. Tagged cardiac MRI allows the measurement of anatomical and functional myocardial parameters. This protocol generates a dark grid which is deformed with the myocardium displacement on both Short-Axis (SA) and Long-Axis (LA) frames in a time sequence. Visual evaluation of the grid deformation allows the estimation of the displacement inside the myocardium. The work described in this paper aims to make robust and reliable the visual enhancement of the grid tags on cardiac MRI sequences, thanks to an informational formalism based on Extreme Physical Informational (EPI). This approach leads to the development of an original diffusion pre-processing allowing us to make better the robustness of the visual detection and the following of the grid of tags.

Security Analysis of a Block Encryption Algorithm Based on Dynamic Sequences of Multiple Chaotic Systems

Recently, the cryptosystem based on chaos has attracted much attention. Wang and Yu （Commun. Nonlin. Sci. Numer. Simulat. 14（2009）574） proposed a block encryption algorithm based on dynamic sequences of multiple chaotic systems. We analyze the potential flaws in the algorithm. Then, a chosen-plaintext attack is presented. Some remedial measures are suggested to avoid the flaws effectively. Furthermore, an improved encryption algorithm is proposed to resist the attacks＂ and to keep all the merits of the original cryptosystem.

On physical education of vocational high school students under the context of IT development

The integration of information technology and the continuous development of the information network make the college physical education degree of information technology constantly improving. College physical education is not only formed in the internal network, then sharing information resources and making sports teaching organization efficient as a whole, but communicate with the external network, formatting the Internet and producing significant changes in the teaching environment of college sports and sports teaching. College physical education is facing the reformation of better educated and digitization, virtualization, molecules, networking, agile and globalization. Physical education and sports teaching degree of information is closely related.

ESR-PINNs:Physics-informed neural networks with expansion-shrinkage resampling selection strategies

Neural network methods have been widely used in many fields of scientific research with the rapid increase of computing power.The physics-informed neural networks(PINNs)have received much attention as a major breakthrough in solving partial differential equations using neural networks.In this paper,a resampling technique based on the expansion-shrinkage point(ESP)selection strategy is developed to dynamically modify the distribution of training points in accordance with the performance of the neural networks.In this new approach both training sites with slight changes in residual values and training points with large residuals are taken into account.In order to make the distribution of training points more uniform,the concept of continuity is further introduced and incorporated.This method successfully addresses the issue that the neural network becomes ill or even crashes due to the extensive alteration of training point distribution.The effectiveness of the improved physics-informed neural networks with expansion-shrinkage resampling is demonstrated through a series of numerical experiments.

Physical informed memory networks for solving PDEs:implementation and applications

With the advent of physics informed neural networks(PINNs),deep learning has gained interest for solving nonlinear partial differential equations(PDEs)in recent years.In this paper,physics informed memory networks(PIMNs)are proposed as a new approach to solving PDEs by using physical laws and dynamic behavior of PDEs.Unlike the fully connected structure of the PINNs,the PIMNs construct the long-term dependence of the dynamics behavior with the help of the long short-term memory network.Meanwhile,the PDEs residuals are approximated using difference schemes in the form of convolution filter,which avoids information loss at the neighborhood of the sampling points.Finally,the performance of the PIMNs is assessed by solving the Kd V equation and the nonlinear Schr?dinger equation,and the effects of difference schemes,boundary conditions,network structure and mesh size on the solutions are discussed.Experiments show that the PIMNs are insensitive to boundary conditions and have excellent solution accuracy even with only the initial conditions.

Action Origin of the Cosmos

In physical information theory elementary objects are represented as correlation structures with oscillator properties and characterized by action. The procedure makes it possible to describe the photons of positive and negative charges by positive and negative real action;gravitons are represented in equal amounts by positive and negative real, i.e., virtual action, and the components of the vacuum are characterized by deactivated virtual action. An analysis of the currents in the correlation structures of photons of static Maxwell fields with wave and particle properties, of the Maxwell vacuum and of the gravitons leads to a uniform three-dimensional representation of the structure of the action. Based on these results, a basic structure consisting of a system of oscillators is proposed, which describe the properties of charges and masses and interact with the photons of static Maxwell fields and with gravitons. All properties of the elemental components of nature can thus be traced back to a basic structure of action. It follows that nature can be derived from a uniform structure and this structure of action must therefore also be the basis of the origin of the cosmos.

Physics informed machine learning: Seismic wave equation

Similar to many fields of sciences,recent deep learning advances have been applied extensively in geosciences for both small-and large-scale problems.However,the necessity of using large training data and the’black box’nature of learning have limited them in practice and difficult to interpret.Furthermore,including the governing equations and physical facts in such methods is also another challenge,which entails either ignoring the physics or simplifying them using unrealistic data.To address such issues,physics informed machine learning methods have been developed which can integrate the governing physics law into the learning process.In this work,a 1-dimensional(1 D)time-dependent seismic wave equation is considered and solved using two methods,namely Gaussian process(GP)and physics informed neural networks.We show that these meshless methods are trained by smaller amount of data and can predict the solution of the equation with even high accuracy.They are also capable of inverting any parameter involved in the governing equation such as wave velocity in our case.Results show that the GP can predict the solution of the seismic wave equation with a lower level of error,while our developed neural network is more accurate for velocity(P-and S-wave)and density inversion.

Evolution of a Thermo Vacuum State in a Single-Mode Amplitude Dissipative Channel

We investigate how an initial thermo vacuum state, in the context of thermo field dynamics, evolves in a single-mode amplitude dissipative channel, and find that in this process the thermo squeezing effect decreases while the fictitious-mode vacuum becomes chaotic.

Transformation Operator and Criterion for Perfectly Teleporting Arbitrary Three-Qubit State with Six-Qubit Channel and Bell-State Measurement

Using the method presented recently [Phys.Rev.A 77（2008）014306; Phys.Lett.A 369（2007）377], the transformation operator （TO） is explicitly given for teleporting an arbitrary three-qubit state with a six-qubit channel and Bell-state measurements. A criterion on whether such quantum teleportation can be perfectly realized is educed in terms of TO. Moreover, six instantiations on TO and criterion are concisely shown.

Physics-constrained indirect supervised learning

This study proposes a supervised learning method that does not rely on labels.We use variables associated with the label as indirect labels,and construct an indirect physics-constrained loss based on the physical mechanism to train the model.In the training process,the model prediction is mapped to the space of value that conforms to the physical mechanism through the projection matrix,and then the model is trained based on the indirect labels.The final prediction result of the model conforms to the physical mechanism between indirect label and label,and also meets the constraints of the indirect label.The present study also develops projection matrix normalization and prediction covariance analysis to ensure that the model can be fully trained.Finally,the effect of the physics-constrained indirect supervised learning is verified based on a well log generation problem.

Ground State Eigenfunction of Spheroidal Wave Functions

We study the spin-weighted spheroidal wave functions in the case of s = m = 0. Their eigenvalue problem is investigated by the perturbation method in supersymmetric quantum mechanics. In the first three terms of parameter a = a^2 w^2, the ground eigenvalue and eigenfunction are obtained. The obtained ground eigenfunction is elegantly in dosed forms. These results are new and very useful for the application of the spheroidal wave functions.

Probabilistic Teleportation of Two-Particle State of General Formation in Ion Trap

We propose a scheme for the probabilistic teleportation of an unknown two-particle state of general formation in ion trap. It is shown that one can realize experimentally this teleportation protocol of two-particle state with presently available techniques.

Analytical Solutions of the Manning-Rosen Potential In the Tridiagonal Program

The Schr？dinger equation with the Manning-Rosen potential is studied by working in a complete square integrable basis that carries a tridiagonal matrix representation of the wave operator. In this program, solving the Schr？dinger equation is translated into finding solutions of the resulting three-term recursion relation for the expansion coefficients of the wavefunction. The discrete spectrum of the bound states is obtained by diagonalization of the recursion relation with special choice of the parameters and the wavefunctions is expressed in terms of the Jocobi polynomial.

Generalized Coherent States of a Particle in a Time-Dependent Linear Potential

We derive, with an invariant operator method and unitary transformation approach, that the Schr6dinger equation with a time-dependent linear potential possesses an infinite string of shape-preseving wave-packet states 1φ,λ （t） having classical motion. The qualitative properties of the invariant eigenvalue spectrum （discrete or continuous） are described separately for the different values of the frequency ω of a harmonic oscillator. It is also shown that, for a discrete eigenvalue spectrum, the states 1φ,λ （t） could be obtained from the coherent state 1φ,λ （t）.

Scheme for Generating Cluster States with Charge Qubits in a Cavity

Based on superconducting charge qubits （SCCQs） coupled to a single-mode microwave cavity, we propose a scheme for generating charge cluster states. For all SCCQs, the controlled gate voltages are all in their degeneracy points, the quantum information is encoded in two logic states of charge basis. The generation of the multi-qubit cluster state can be achieved step by step on a pair of nearest-neighbor qubits. Considering effective long-rang coupling, we provide an efficient way to one-step generating of a highly entangled cluster state, in which the qubit-qubit coupling is mediated by the cavity mode. Our quantum operations are insensitive to the initial state of the cavity mode by removing the influence of the cavity mode via the periodical evolution of the system. Thus, our operation may be against the decoherence from the cavity.

Discovering Phase Field Models from Image Data with the Pseudo-Spectral Physics Informed Neural Networks

In this paper,we introduce a new deep learning framework for discovering the phase-field models from existing image data.The new framework embraces the approximation power of physics informed neural networks(PINNs)and the computational efficiency of the pseudo-spectral methods,which we named pseudo-spectral PINN or SPINN.Unlike the baseline PINN,the pseudo-spectral PINN has several advantages.First of all,it requires less training data.A minimum of two temporal snapshots with uniform spatial resolution would be adequate.Secondly,it is computationally efficient,as the pseudo-spectral method is used for spatial discretization.Thirdly,it requires less trainable parameters compared with the baseline PINN,which significantly simplifies the training process and potentially assures fewer local minima or saddle points.We illustrate the effectiveness of pseudo-spectral PINN through several numerical examples.The newly proposed pseudo-spectral PINN is rather general,and it can be readily applied to discover other FDE-based models from image data.