This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred...This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.展开更多
Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current t...Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.展开更多
The 2024 MRE HP Special Volume selects papers on new theoretical and experimental developments in the use of static largevolume presses(LVPs)1–3 and dynamic compression4,5 for studies under extreme high-pressure and ...The 2024 MRE HP Special Volume selects papers on new theoretical and experimental developments in the use of static largevolume presses(LVPs)1–3 and dynamic compression4,5 for studies under extreme high-pressure and high-temperature(HPHT)conditions.It also continues the previous year’s6 contemporary focus on superhydrides7–11 with extremely high superconducting temperatures Tc and addresses some controversial issues.12–14 In addition,it explores unconventional pressure-induced chemistry,particularly novel chemical stoichiometry and its impact on geochemistry and cosmochemistry in the deep interiors of Earth and other planets.18–21.展开更多
Magnets exhibiting the Kitaev interaction,a bond-dependent magnetic interaction in honeycomb lattices,are generally regarded as promising candidates for hosting novel phenomena like quantum spin liquid states.However,...Magnets exhibiting the Kitaev interaction,a bond-dependent magnetic interaction in honeycomb lattices,are generally regarded as promising candidates for hosting novel phenomena like quantum spin liquid states.However,realizing such magnets remains a significant challenge.Recently,some studies have suggested honeycomb magnets A_(3)Ni_(2)XO_(6)(A=Li,Na;X=Bi,Sb)with a high spin S=1 could serve as potential candidates for realizing strong Kitaev interactions.In this work,we systematically investigate their magnetic properties,with a particular emphasis on their Kitaev interactions,using first-principles calculations and Monte Carlo simulations.Our results indicate that all A_(3)Ni_(2)XO_(6)compounds are zigzag antiferromagnets,and their magnetic moments almost tend to be out of plane.We find that their dominant magnetic interactions are the nearest-neighbor ferromagnetic and third-nearest-neighbor antiferromagnetic Heisenberg interactions,while their Kitaev interactions are extremely weak.By analyzing their electronic structures and the mechanism of generating their magnetic interactions,we reveal that either artificially tuning spin-orbit coupling or applying strain cannot produce sufficient spin-orbit entangled states to realize the intriguing Kitaev interactions.Our work advances the understanding of the magnetism in A_(3)Ni_(2)XO_(6)compounds and provides insights for further exploration of Kitaev physics in honeycomb magnets.展开更多
Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process...Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process.An inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping phenomenon.Deep learning methods have been applied in musculoskeletal imaging,but need a large amount of data for training.Inspired by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and density.In the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue models.The results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best performance.The specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.展开更多
Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.Howev...Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.展开更多
The task of achieving high-accuracy full-field reconstruction in the realm of water waves is widely acknowledged as a challenge,primarily due to the sparsity and incompleteness of data measurement in both temporal and...The task of achieving high-accuracy full-field reconstruction in the realm of water waves is widely acknowledged as a challenge,primarily due to the sparsity and incompleteness of data measurement in both temporal and spatial dimensions.We develop a full-field velocity and pressure reconstruction approach for non-linear water waves based on physics-informed neural networks from the free surface measurement.The fully non-linear highly dispersive Boussinesq model is integrated to reduce the training cost by representing the three dimensional water wave problems in the horizontal two-dimensional plane with the inherent veloc-ity distribution along water depth.A series of test cases,including the solitary waves,fifth-order Stokes waves,standing waves,and superimposed waves,are employed to evaluate the performance of the algorithm.The proposed novel neural networks are capable of accurately reconstructing the flow fields even when assimilating the limited and sparse free surface deformation data,which facilitates the development of detecting the flow characteristics in real ocean waves.展开更多
In physics,our expectations for system behavior are often guided by intuitive arithmetic.For systems composed of identical units,we anticipate synergy of the contributions from these units,where 1+1=2.Conversely,for s...In physics,our expectations for system behavior are often guided by intuitive arithmetic.For systems composed of identical units,we anticipate synergy of the contributions from these units,where 1+1=2.Conversely,for systems built from opposing units,we expect cancellation of their contributions,where 1-1=0.This intuitive arithmetic has long underpinned our understanding of physical properties of materials,from electronic transport to optical responses.However,scientific breakthroughs often occur when nature reveals ways to circumvent these seemingly fundamental rules,opening new possibilities that challenge our deepest assumptions about material behavior.展开更多
Machine learning-based modeling of reactor physics problems has attracted increasing interest in recent years.Despite some progress in one-dimensional problems,there is still a paucity of benchmark studies that are ea...Machine learning-based modeling of reactor physics problems has attracted increasing interest in recent years.Despite some progress in one-dimensional problems,there is still a paucity of benchmark studies that are easy to solve using traditional numerical methods albeit still challenging using neural networks for a wide range of practical problems.We present two networks,namely the Generalized Inverse Power Method Neural Network(GIPMNN)and Physics-Constrained GIPMNN(PC-GIPIMNN)to solve K-eigenvalue problems in neutron diffusion theory.GIPMNN follows the main idea of the inverse power method and determines the lowest eigenvalue using an iterative method.The PC-GIPMNN additionally enforces conservative interface conditions for the neutron flux.Meanwhile,Deep Ritz Method(DRM)directly solves the smallest eigenvalue by minimizing the eigenvalue in Rayleigh quotient form.A comprehensive study was conducted using GIPMNN,PC-GIPMNN,and DRM to solve problems of complex spatial geometry with variant material domains from the fleld of nuclear reactor physics.The methods were compared with the standard flnite element method.The applicability and accuracy of the methods are reported and indicate that PC-GIPMNN outperforms GIPMNN and DRM.展开更多
Digital rock physics(DRP)is a paramount technology to improve the economic benefits of oil and gas fields,devise more scientific oil and gas field development plans,and create digital oil and gas fields.Currently,a si...Digital rock physics(DRP)is a paramount technology to improve the economic benefits of oil and gas fields,devise more scientific oil and gas field development plans,and create digital oil and gas fields.Currently,a significant gap is present between DRP theory and practical applications.Conventional digital-core construction focuses only on simple cores,and the recognition and segmentation effect of fractures and pores of complex cores is poor.The identification of rock minerals is inaccurate,which leads to the difference between the digital and actual cores.To promote the application of DRP in developing oil and gas fields,based on the high-precision X-ray computed tomography scanning technology,the U-Net deep learning model of the full convolution neural network is used to segment the pores,fractures,and matrix from the complex rock core with natural fractures innovatively.Simultaneously,the distribution of rock minerals is divided,and the distribution of rock conditions is corrected by X-ray diffraction.A pore—fracture network model is established based on the equivalent radius,which lays the foundation for fluid seepage simulation.Finally,the accuracy of the established a digital core is verified by the porosity measured via nuclear magnetic resonance technology,which is of great significance to the development and application of DRP in oil and gas fields.展开更多
Magnetospheric physics has been one of the most active areas in Chinese space research in past two years. The major project "Energy Transport Processes in the Solar-Terrestrial System" (1993-1997) sponsored ...Magnetospheric physics has been one of the most active areas in Chinese space research in past two years. The major project "Energy Transport Processes in the Solar-Terrestrial System" (1993-1997) sponsored by the National Natural Science Foundation in China (NSFC) has been successfully completed. Prestudies relevant to the key scientific engineering program "Meridian Chain at One Hundred Twenty Degree East Multi-Station and Multi-Instrument Observatory System" have started. A new key project "Study of Auroral Magnetospheric and Ionospheric Physics" (1997-1999) sponsored by the NSFC has begun. The Space Active Experiment Program has been carrying on further.Collaborations between Chinese and international magnetospheric physicists have proceeded forward. More than 40 papers covering a variety of subjects in the magnetospheric physics were published in Chinese and international academic journals. Most of these works were supported by the NSFC. This report provides a brief summary of aforementioned advances made in China in the past two years.展开更多
This brief report presents the latest advances of the magnetospheric physics researches in China during the period of 2002-2004. The progress of the magnetospheric space mission DSP is given in another dedicated paper...This brief report presents the latest advances of the magnetospheric physics researches in China during the period of 2002-2004. The progress of the magnetospheric space mission DSP is given in another dedicated paper of this issue.展开更多
Their brief report presents the advances of the magnetospheric physics researches in China during the period of 2004-2006. During the past two years, China-ESA cooperation DSP (Double Star Program) satellites were suc...Their brief report presents the advances of the magnetospheric physics researches in China during the period of 2004-2006. During the past two years, China-ESA cooperation DSP (Double Star Program) satellites were successively launched. In addition, China also participated in the scientific research of ESA's Cluster mission. The DSP and Cluster missions provide Chinese space physicists high quality data to study multiscale physical process in the magnetosphere. The work made based on the data of DSP is presented in the paper of "Progress of Double Star Program" of this issue.展开更多
This brief report presents the latest advances of the magnetospheric physics researches in China during the period of 2000-2002, made independently by Chinese space physicists and through international cooperation. Th...This brief report presents the latest advances of the magnetospheric physics researches in China during the period of 2000-2002, made independently by Chinese space physicists and through international cooperation. The related areas cover almost every aspect of magnetospheric physics.展开更多
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 breakthr...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.展开更多
Experiments executed by author of the present article (period 1968-1992) showed that the magnetic spinorial particles (magnetic charges) are real structural components of atoms and substance and are immediate sources ...Experiments executed by author of the present article (period 1968-1992) showed that the magnetic spinorial particles (magnetic charges) are real structural components of atoms and substance and are immediate sources of all magnetic fields in Nature. Joint orbital currents of electric and magnetic charges within atomic shells are the natural sources of gravitational field which is a vortical electromagnetic field. The vector nature of the gravitational field, in essence, is analogous to the vortical magnetic field that allows entering in the physical representations of such States of the gravitational field as paragravitation and ferrogravitation. Physical masses (atoms, substance, etc.), which emit ferrogravitational field, are repelled by sources paragravitational field, for example, from Earth. It is a manifestation of the effect of levitation, which was discovered by the author of this article. The forces of the technical levitation, which are formed by technical ferrogravitational fields, can be used in transport, lifting and space technology, energy and many other areas of human activity. The main reason that the real magnetic charges were “buried alive” in modern theoretical physics is the conditions of their confinement in the structures of atoms and substance, which is radically different from the confinement of electrons. Very negative role is played here by erroneous electromagnetic concept Maxwell, in which the magnetic field was officially deprived of their own source: magnetic pole or magnetic charge.展开更多
The magnetic spinor particles (magnetic charges) are the real structural components all varieties of the Mass, for example, atoms, nucleons, positrons and neutrinos. Atomic-shaped device of Mass is the natural and the...The magnetic spinor particles (magnetic charges) are the real structural components all varieties of the Mass, for example, atoms, nucleons, positrons and neutrinos. Atomic-shaped device of Mass is the natural and the only possible organization of electric and magnetic charges which can create a gravitational field. At level of a popular language one can define nucleons as “small atoms”, and positron and neutrino as “very small atoms”. The electric and magnetic fundamental particles in neutron and proton shells which by tradition should be called quarks have charges of smaller magnitude than the charges of particles in atomic shells. Positron which participates in the gravitational interaction and, consequently, has an atomic-shaped device is the most likely candidate for the role of the proton nucleus. The most likely candidate particles on the participation in nuclei of proton and neutron as well as in nuclei of the positron and neutrino are presented in the article. So-called abnormal magnetic moment of neutron is formed by the quark magnetic dipoles which are like to unpaired electrons in the so-called magnetic atoms rotate on the outer orbitals of the neutron shell. The participation of the “magnetic electron” (magneton) in the neutrino core assumes the existence of the so-called anomalous magnetic moment and in the neutrino shell. The existence of real magnetic charges in the structures of the Mass draws our attention on such important problem as interaction between charges in the framework of electromagnetic dipoles such as and in which manifest the weak attraction. Weak interaction by its nature is electromagnetic. So-called electromagnetic interaction, manifested in pairs of homogeneous charges of opposite signs, is either electric or magnetic, but not electromagnetic. The explanation of the weak interaction in the marked pairs of charges is based on the author’s concept of the World Physical Triad and “Dark Energy”. Forces responsible for the interaction of the charges composing the electromagnetic dipoles correspond, conditionally of the weak charges of the particles which what assume mutual suppression of the influence of their fields on the Energo-medium and the formation of the weak “Dark energy”. Complex of magnetic particles, the quark magnetic dipoles and magneton by means of which the interconversion of a proton and a neutron is realized and maintained their constant number in the atomic nuclei can be called as magnetic meson. Namely, a processes of interconversion between a neutron and a proton which, as a rule, are not accompanied by secretions, created the illusion of neutron stability in atomic nuclei. The energy created by an exchange of magnetic mesons between neutron and proton can be a component of nuclear forces (strong interaction). Another effective and, most likely, the main component in the composition of the nuclear forces is the gravitational “Dark Energy”. Physics and structure of neutrinos presented in the paper suggest that the nature of these particles closer to the ideology of E. Majorana than P. Dirac’s.展开更多
In the past two years, many progresses have been made in magnetospheric physics by using the data of Double Star Program, Cluster, THEMIS and RBSP missions, or by computer simulations. This paper briefly reviews these...In the past two years, many progresses have been made in magnetospheric physics by using the data of Double Star Program, Cluster, THEMIS and RBSP missions, or by computer simulations. This paper briefly reviews these works based on papers selected from the 126 publications from March 2012 to March 2014. The subjects cover various sub-branches of magnetospheric physics,including geomagnetic storm, magnetospheric substorm and magnetic reconnection.展开更多
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 ...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.展开更多
In the past two years,many progresses are made in magnetospheric physics by using either the data of Double Star Program,Cluster and THEMIS missions,or by computer simulations. This paper briefly reviews these works b...In the past two years,many progresses are made in magnetospheric physics by using either the data of Double Star Program,Cluster and THEMIS missions,or by computer simulations. This paper briefly reviews these works based on papers selected from the 80 publications from April 2010 to April 2011.The subjects covered various sub-branches of magnetospheric physics,including geomagnetic storm,magnetospheric substorm,etc.展开更多
基金supported by the National Key R&D Program of China[grant number 2023YFC3008004]。
文摘This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.
基金the North Dakota Industrial Commission (NDIC) for their financial supportprovided by the University of North Dakota Computational Research Center。
文摘Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.
基金financial support from the Shanghai Key Laboratory of MFree,China(Grant No.22dz2260800)the Shanghai Science and Technology Committee,China(Grant No.22JC1410300).
文摘The 2024 MRE HP Special Volume selects papers on new theoretical and experimental developments in the use of static largevolume presses(LVPs)1–3 and dynamic compression4,5 for studies under extreme high-pressure and high-temperature(HPHT)conditions.It also continues the previous year’s6 contemporary focus on superhydrides7–11 with extremely high superconducting temperatures Tc and addresses some controversial issues.12–14 In addition,it explores unconventional pressure-induced chemistry,particularly novel chemical stoichiometry and its impact on geochemistry and cosmochemistry in the deep interiors of Earth and other planets.18–21.
基金supported by the National Key R&D Program of China(Grant Nos.2024-YFA1408303 and 2022YFA1403301)the National Natural Sciences Foundation of China(Grant Nos.12474247 and 92165204)+1 种基金support from Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices(Grant No.2022B1212010008)Research Center for Magnetoelectric Physicsof Guangdong Province(Grant No.2024B0303390001).
文摘Magnets exhibiting the Kitaev interaction,a bond-dependent magnetic interaction in honeycomb lattices,are generally regarded as promising candidates for hosting novel phenomena like quantum spin liquid states.However,realizing such magnets remains a significant challenge.Recently,some studies have suggested honeycomb magnets A_(3)Ni_(2)XO_(6)(A=Li,Na;X=Bi,Sb)with a high spin S=1 could serve as potential candidates for realizing strong Kitaev interactions.In this work,we systematically investigate their magnetic properties,with a particular emphasis on their Kitaev interactions,using first-principles calculations and Monte Carlo simulations.Our results indicate that all A_(3)Ni_(2)XO_(6)compounds are zigzag antiferromagnets,and their magnetic moments almost tend to be out of plane.We find that their dominant magnetic interactions are the nearest-neighbor ferromagnetic and third-nearest-neighbor antiferromagnetic Heisenberg interactions,while their Kitaev interactions are extremely weak.By analyzing their electronic structures and the mechanism of generating their magnetic interactions,we reveal that either artificially tuning spin-orbit coupling or applying strain cannot produce sufficient spin-orbit entangled states to realize the intriguing Kitaev interactions.Our work advances the understanding of the magnetism in A_(3)Ni_(2)XO_(6)compounds and provides insights for further exploration of Kitaev physics in honeycomb magnets.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12122403 and 12327807).
文摘Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process.An inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping phenomenon.Deep learning methods have been applied in musculoskeletal imaging,but need a large amount of data for training.Inspired by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and density.In the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue models.The results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best performance.The specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.
基金supported by the National Natural Science Foundation of China(Grant Nos.1217211 and 12372244).
文摘Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.
基金supported by the Narional Nanre Science Foundarion of China(Grand Nos 12202272 and U22B6010)。
文摘The task of achieving high-accuracy full-field reconstruction in the realm of water waves is widely acknowledged as a challenge,primarily due to the sparsity and incompleteness of data measurement in both temporal and spatial dimensions.We develop a full-field velocity and pressure reconstruction approach for non-linear water waves based on physics-informed neural networks from the free surface measurement.The fully non-linear highly dispersive Boussinesq model is integrated to reduce the training cost by representing the three dimensional water wave problems in the horizontal two-dimensional plane with the inherent veloc-ity distribution along water depth.A series of test cases,including the solitary waves,fifth-order Stokes waves,standing waves,and superimposed waves,are employed to evaluate the performance of the algorithm.The proposed novel neural networks are capable of accurately reconstructing the flow fields even when assimilating the limited and sparse free surface deformation data,which facilitates the development of detecting the flow characteristics in real ocean waves.
基金supported by the National Natural Science Foundation of China (Grant No.12374109)the National Key Research and Development Program of China (Grant No.2023YFA1406600)。
文摘In physics,our expectations for system behavior are often guided by intuitive arithmetic.For systems composed of identical units,we anticipate synergy of the contributions from these units,where 1+1=2.Conversely,for systems built from opposing units,we expect cancellation of their contributions,where 1-1=0.This intuitive arithmetic has long underpinned our understanding of physical properties of materials,from electronic transport to optical responses.However,scientific breakthroughs often occur when nature reveals ways to circumvent these seemingly fundamental rules,opening new possibilities that challenge our deepest assumptions about material behavior.
基金partially supported by the National Natural Science Foundation of China(No.11971020)Natural Science Foundation of Shanghai(No.23ZR1429300)Innovation Funds of CNNC(Lingchuang Fund)。
文摘Machine learning-based modeling of reactor physics problems has attracted increasing interest in recent years.Despite some progress in one-dimensional problems,there is still a paucity of benchmark studies that are easy to solve using traditional numerical methods albeit still challenging using neural networks for a wide range of practical problems.We present two networks,namely the Generalized Inverse Power Method Neural Network(GIPMNN)and Physics-Constrained GIPMNN(PC-GIPIMNN)to solve K-eigenvalue problems in neutron diffusion theory.GIPMNN follows the main idea of the inverse power method and determines the lowest eigenvalue using an iterative method.The PC-GIPMNN additionally enforces conservative interface conditions for the neutron flux.Meanwhile,Deep Ritz Method(DRM)directly solves the smallest eigenvalue by minimizing the eigenvalue in Rayleigh quotient form.A comprehensive study was conducted using GIPMNN,PC-GIPMNN,and DRM to solve problems of complex spatial geometry with variant material domains from the fleld of nuclear reactor physics.The methods were compared with the standard flnite element method.The applicability and accuracy of the methods are reported and indicate that PC-GIPMNN outperforms GIPMNN and DRM.
基金Science and Technology Cooperation Project of the CNPC-SWPU Innovation Alliance(No.2020CX010501)National Science and Technology Major ProjectNational Natural Science Foundation of China Petrochemical Joint Fund Project(U1762107)
文摘Digital rock physics(DRP)is a paramount technology to improve the economic benefits of oil and gas fields,devise more scientific oil and gas field development plans,and create digital oil and gas fields.Currently,a significant gap is present between DRP theory and practical applications.Conventional digital-core construction focuses only on simple cores,and the recognition and segmentation effect of fractures and pores of complex cores is poor.The identification of rock minerals is inaccurate,which leads to the difference between the digital and actual cores.To promote the application of DRP in developing oil and gas fields,based on the high-precision X-ray computed tomography scanning technology,the U-Net deep learning model of the full convolution neural network is used to segment the pores,fractures,and matrix from the complex rock core with natural fractures innovatively.Simultaneously,the distribution of rock minerals is divided,and the distribution of rock conditions is corrected by X-ray diffraction.A pore—fracture network model is established based on the equivalent radius,which lays the foundation for fluid seepage simulation.Finally,the accuracy of the established a digital core is verified by the porosity measured via nuclear magnetic resonance technology,which is of great significance to the development and application of DRP in oil and gas fields.
文摘Magnetospheric physics has been one of the most active areas in Chinese space research in past two years. The major project "Energy Transport Processes in the Solar-Terrestrial System" (1993-1997) sponsored by the National Natural Science Foundation in China (NSFC) has been successfully completed. Prestudies relevant to the key scientific engineering program "Meridian Chain at One Hundred Twenty Degree East Multi-Station and Multi-Instrument Observatory System" have started. A new key project "Study of Auroral Magnetospheric and Ionospheric Physics" (1997-1999) sponsored by the NSFC has begun. The Space Active Experiment Program has been carrying on further.Collaborations between Chinese and international magnetospheric physicists have proceeded forward. More than 40 papers covering a variety of subjects in the magnetospheric physics were published in Chinese and international academic journals. Most of these works were supported by the NSFC. This report provides a brief summary of aforementioned advances made in China in the past two years.
文摘This brief report presents the latest advances of the magnetospheric physics researches in China during the period of 2002-2004. The progress of the magnetospheric space mission DSP is given in another dedicated paper of this issue.
基金Supported by the National Natural Science Foundation of China (40523006, 40390153, 40474062), International Collaboration Research Team Program and Bairen Plan of Chinese Academy of Sciences
文摘Their brief report presents the advances of the magnetospheric physics researches in China during the period of 2004-2006. During the past two years, China-ESA cooperation DSP (Double Star Program) satellites were successively launched. In addition, China also participated in the scientific research of ESA's Cluster mission. The DSP and Cluster missions provide Chinese space physicists high quality data to study multiscale physical process in the magnetosphere. The work made based on the data of DSP is presented in the paper of "Progress of Double Star Program" of this issue.
基金Supported by the National Natural Science Foundation of China through grant No.40025413
文摘This brief report presents the latest advances of the magnetospheric physics researches in China during the period of 2000-2002, made independently by Chinese space physicists and through international cooperation. The related areas cover almost every aspect of magnetospheric physics.
基金Project supported by the National Key Research and Development Program of China(Grant No.2020YFC1807905)the National Natural Science Foundation of China(Grant Nos.52079090 and U20A20316)the Basic Research Program of Qinghai Province(Grant No.2022-ZJ-704).
文摘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.
文摘Experiments executed by author of the present article (period 1968-1992) showed that the magnetic spinorial particles (magnetic charges) are real structural components of atoms and substance and are immediate sources of all magnetic fields in Nature. Joint orbital currents of electric and magnetic charges within atomic shells are the natural sources of gravitational field which is a vortical electromagnetic field. The vector nature of the gravitational field, in essence, is analogous to the vortical magnetic field that allows entering in the physical representations of such States of the gravitational field as paragravitation and ferrogravitation. Physical masses (atoms, substance, etc.), which emit ferrogravitational field, are repelled by sources paragravitational field, for example, from Earth. It is a manifestation of the effect of levitation, which was discovered by the author of this article. The forces of the technical levitation, which are formed by technical ferrogravitational fields, can be used in transport, lifting and space technology, energy and many other areas of human activity. The main reason that the real magnetic charges were “buried alive” in modern theoretical physics is the conditions of their confinement in the structures of atoms and substance, which is radically different from the confinement of electrons. Very negative role is played here by erroneous electromagnetic concept Maxwell, in which the magnetic field was officially deprived of their own source: magnetic pole or magnetic charge.
文摘The magnetic spinor particles (magnetic charges) are the real structural components all varieties of the Mass, for example, atoms, nucleons, positrons and neutrinos. Atomic-shaped device of Mass is the natural and the only possible organization of electric and magnetic charges which can create a gravitational field. At level of a popular language one can define nucleons as “small atoms”, and positron and neutrino as “very small atoms”. The electric and magnetic fundamental particles in neutron and proton shells which by tradition should be called quarks have charges of smaller magnitude than the charges of particles in atomic shells. Positron which participates in the gravitational interaction and, consequently, has an atomic-shaped device is the most likely candidate for the role of the proton nucleus. The most likely candidate particles on the participation in nuclei of proton and neutron as well as in nuclei of the positron and neutrino are presented in the article. So-called abnormal magnetic moment of neutron is formed by the quark magnetic dipoles which are like to unpaired electrons in the so-called magnetic atoms rotate on the outer orbitals of the neutron shell. The participation of the “magnetic electron” (magneton) in the neutrino core assumes the existence of the so-called anomalous magnetic moment and in the neutrino shell. The existence of real magnetic charges in the structures of the Mass draws our attention on such important problem as interaction between charges in the framework of electromagnetic dipoles such as and in which manifest the weak attraction. Weak interaction by its nature is electromagnetic. So-called electromagnetic interaction, manifested in pairs of homogeneous charges of opposite signs, is either electric or magnetic, but not electromagnetic. The explanation of the weak interaction in the marked pairs of charges is based on the author’s concept of the World Physical Triad and “Dark Energy”. Forces responsible for the interaction of the charges composing the electromagnetic dipoles correspond, conditionally of the weak charges of the particles which what assume mutual suppression of the influence of their fields on the Energo-medium and the formation of the weak “Dark energy”. Complex of magnetic particles, the quark magnetic dipoles and magneton by means of which the interconversion of a proton and a neutron is realized and maintained their constant number in the atomic nuclei can be called as magnetic meson. Namely, a processes of interconversion between a neutron and a proton which, as a rule, are not accompanied by secretions, created the illusion of neutron stability in atomic nuclei. The energy created by an exchange of magnetic mesons between neutron and proton can be a component of nuclear forces (strong interaction). Another effective and, most likely, the main component in the composition of the nuclear forces is the gravitational “Dark Energy”. Physics and structure of neutrinos presented in the paper suggest that the nature of these particles closer to the ideology of E. Majorana than P. Dirac’s.
文摘In the past two years, many progresses have been made in magnetospheric physics by using the data of Double Star Program, Cluster, THEMIS and RBSP missions, or by computer simulations. This paper briefly reviews these works based on papers selected from the 126 publications from March 2012 to March 2014. The subjects cover various sub-branches of magnetospheric physics,including geomagnetic storm, magnetospheric substorm and magnetic reconnection.
基金supported by the Researchers Supporting Project number (RSPD2024R526),King Saud University,Riyadh,Saudi Arabi.
文摘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.
文摘In the past two years,many progresses are made in magnetospheric physics by using either the data of Double Star Program,Cluster and THEMIS missions,or by computer simulations. This paper briefly reviews these works based on papers selected from the 80 publications from April 2010 to April 2011.The subjects covered various sub-branches of magnetospheric physics,including geomagnetic storm,magnetospheric substorm,etc.
