Students' thinking in physics learning has changed from junior high school to senior high school. However, the teaching method of physics in senior high school has not changed with the change of students' thin...Students' thinking in physics learning has changed from junior high school to senior high school. However, the teaching method of physics in senior high school has not changed with the change of students' thinking mode. As a result, students have difficulties in the process of acceptance and their grades have declined. With the development of curriculum reform, traditional teaching has not adapted to the transition requirements of junior high school. This paper applies the research methods of interview, observation and comparison to dissect students' learning behavior, teachers' teaching behavior, teaching materials and other aspects, trying to find out the reasons for the "gap" in physics classroom teaching in junior and senior high schools, and puts forward the solutions to the problem.展开更多
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 ...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.展开更多
The surrogate model serves as an efficient simulation tool during the slope parameter inversion process.However,the creep constitutive model integrated with dynamic damage evolution poses challenges in development of ...The surrogate model serves as an efficient simulation tool during the slope parameter inversion process.However,the creep constitutive model integrated with dynamic damage evolution poses challenges in development of the required surrogate model.In this study,a novel physics knowledge-based surrogate model framework is proposed.In this framework,a Transformer module is employed to capture straindriven softening-hardening physical mechanisms.Positional encoding and self-attention are utilized to transform the constitutive parameters associated with shear strain,which are not directly time-related,into intermediate latent features for physical loss calculation.Next,a multi-layer stacked GRU(gated recurrent unit)network is built to provide input interfaces for time-dependent intermediate latent features,hydraulic boundary conditions,and water-rock interaction degradation equations,with static parameters introduced via external fully-connected layers.Finally,a combined loss function is constructed to facilitate the collaborative training of physical and data loss,introducing time-dependent weight adjustments to focus the surrogate model on accurate deformation predictions during critical phases.Based on the deformation of a reservoir bank landslide triggered by impoundment and subsequent restabilization,an elasto-viscoplastic constitutive model that considers water effect and sliding state dependencies is developed to validate the proposed surrogate model framework.The results indicate that the framework exhibits good performance in capturing physical mechanisms and predicting creep behavior,reducing errors by about 30 times compared to baseline models such as GRU and LSTM(long short-term memory),meeting the precision requirements for parameter inversion.Ablation experiments also confirmed the effectiveness of the framework.This framework can also serve as a reference for constructing other creep surrogate models that involve non-time-related across dimensions.展开更多
Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-eng...Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.展开更多
L1 and L2 acquisition, in some respects, are similar. Language development in children goes hand in hand with physical and cognitive development. Children learn their first language by imitation, but not always and no...L1 and L2 acquisition, in some respects, are similar. Language development in children goes hand in hand with physical and cognitive development. Children learn their first language by imitation, but not always and not only by imitation. There seems to be some "innate capacities" that make children start to speak at the same time they do and in the way they do it. Adults learning a second language usually are controlled more by their motivation. But language input is important for both L1 and L2 acquisition. Though there are differences between CL1 and between CL2 and AL2, the way in which these learners acquire some of the grammatical morphemes is similar. This, together with some other evidence, shows that it is not only children who can acquire language. Adults can also acquire a language. But when adults acquire a language, they should also learn it. Some of the ways in which children acquire their language can be used as a model for L2 acquisition, even for Chinese students whose language is unrelated to English and whose culture is different. Learning the culture of the English-speaking countries will benefit the learning of the language. Like children, listening should also be well in advance of speaking in L2 acquisition. To train listening comprehension skills, Asher’s TPR approach proves more effective. TPR approach is at the moment limited to the beginning stage only. In order for students to gain all the five skills in a second language learning, namely, listening, speaking, reading, writing, and interpreting/translating, other methods should be used at the same time, or at later stages.展开更多
Purpose:To identify co-produced multi-stakeholder perspectives important for successful widespread physically active learning(PAL) adoption and implementation.Methods:A total of 35 stakeholders(policymakers n=9;commer...Purpose:To identify co-produced multi-stakeholder perspectives important for successful widespread physically active learning(PAL) adoption and implementation.Methods:A total of 35 stakeholders(policymakers n=9;commercial education sector,n=8;teachers,n=3;researchers,n=15) attended a design thinking PAL workshop.Participants formed 5 multi-disciplinary groups with at least 1 representative from each stakeholder group.Each group,facilitated by a researcher,undertook 2 tasks:(1) using Post-it Notes,the following question was answered:within the school day,what are the opportunities for learning combined with movement?and(2) structured as a washing-line task,the following question was answered:how can we establish PAL as the norm?All discussions were audio-recorded and transcribed.Inductive analyses were conducted by 4 authors.After the analyses were complete,the main themes and subthemes were assigned to 4 predetermined categories:(1) PAL design and implementation,(2) priorities for practice,(3) priorities for policy,and(4) priorities for research.Results:The following were the main themes for PAL implementation:opportunities for PAL within the school day,delivery environments,learning approaches,and the intensity of PAL.The main themes for the priorities for practice included teacher confidence and competence,resources to support delivery,and community of practice.The main themes for the policy for priorities included self-governance,the Office for Standards in Education,Children’s Services,and Skill,policy investment in initial teacher training,and curriculum reform.The main themes for the research priorities included establishing a strong evidence base,school-based PAL implementation,and a whole-systems approach.Conclusion:The present study is the first to identify PAL implementation factors using a combined multi-stakeholder perspective.To achieve wider PAL adoption and implementation,future interventions should be evidence based and address implementation factors at the classroom level(e.g.,approaches and delivery environments),school level(e.g.,communities of practice),and policy level(e.g.,initial teacher training).展开更多
Driven by advancements in artificial intelligence,end-to-end learning has become a key method for system optimization in various fields,including communications.However,applying learning algorithms such as backpropaga...Driven by advancements in artificial intelligence,end-to-end learning has become a key method for system optimization in various fields,including communications.However,applying learning algorithms such as backpropagation directly to communication systems is challenging due to their non-differentiable nature.Existing methods typically require developing a precise differentiable digital model of the physical system,which is computationally complex and can cause significant performance loss after deployment.In response,we propose a novel end-to-end learning framework called physics-guided learning.展开更多
Physical neural networks are artificial neural networks that mimic synapses and neurons using physical systems or materials.These networks harness the distinctive characteristics of physical systems to carry out compu...Physical neural networks are artificial neural networks that mimic synapses and neurons using physical systems or materials.These networks harness the distinctive characteristics of physical systems to carry out computations effectively,potentially surpassing the constraints of conventional digital neural networks.A recent advancement known as“physical self-learning”aims to achieve learning through intrinsic physical processes rather than relying on external computations.This article offers a comprehensive review of the progress made in implementing physical self-learning across various physical systems.Prevailing learning strategies that contribute to the realization of physical self-learning are discussed.Despite challenges in understanding the fundamental mechanism of learning,this work highlights the progress towards constructing intelligent hardware from the ground up,incorporating embedded self-organizing and self-adaptive dynamics in physical systems.展开更多
A novel neural network method is developed for solving systems of conservation laws whose solutions may contain abrupt changes of state,including shock waves and contact discontinuities.In conventional approaches,a lo...A novel neural network method is developed for solving systems of conservation laws whose solutions may contain abrupt changes of state,including shock waves and contact discontinuities.In conventional approaches,a low-cost solution patch is usually used as the input to a neural network for predicting the high-fidelity solution patch.With that technique,however,there is no way to distinguish a smeared discontinuity from a smooth solution with large gradient in the input,and the two almost identical inputs correspond to two fundamentally different high-fidelity solution patches in training and predicting.To circumvent this difficulty,we use local patches of two low-cost numerical solutions of the conservation laws in a converging sequence as the input to a neural network.The neural network then makes a correct prediction by identifying whether the solution contains discontinuities or just smooth variations with large gradients,because the former becomes increasingly steep in a converging sequence in the input,and the latter does not.The inputs can be computed from lowcost numerical schemes with coarse resolution,in a local domain of dependence of a space-time location where the prediction is to be made.Despite smeared input solutions,the output provides sharp approximations of solutions containing shock waves and contact discontinuities.The method works effectively not only for regions with discontinuities,but also for smooth regions of the solution.It is efficient to implement,once trained,and has broader applications for different types of differential equations.展开更多
In our prior work[10],neural networks with local converging inputs(NNLCI)were introduced for solving one-dimensional conservation equations.Two solutions of a conservation law in a converging sequence,computed from lo...In our prior work[10],neural networks with local converging inputs(NNLCI)were introduced for solving one-dimensional conservation equations.Two solutions of a conservation law in a converging sequence,computed from low-cost numerical schemes,and in a local domain of dependence of the space-time location,were used as the input to a neural network in order to predict a high-fidelity solution at a given space-time location.In the present work,we extend the method to twodimensional conservation systems and introduce different solution techniques.Numerical results demonstrate the validity and effectiveness of the NNLCI method for application to multi-dimensional problems.In spite of low-cost smeared input data,the NNLCI method is capable of accurately predicting shocks,contact discontinuities,and the smooth region of the entire field.The NNLCI method is relatively easy to train because of the use of local solvers.The computing time saving is between one and two orders of magnitude compared with the corresponding high-fidelity schemes for two-dimensional Riemann problems.The relative efficiency of the NNLCI method is expected to be substantially greater for problems with higher spatial dimensions or smooth solutions.展开更多
文摘Students' thinking in physics learning has changed from junior high school to senior high school. However, the teaching method of physics in senior high school has not changed with the change of students' thinking mode. As a result, students have difficulties in the process of acceptance and their grades have declined. With the development of curriculum reform, traditional teaching has not adapted to the transition requirements of junior high school. This paper applies the research methods of interview, observation and comparison to dissect students' learning behavior, teachers' teaching behavior, teaching materials and other aspects, trying to find out the reasons for the "gap" in physics classroom teaching in junior and senior high schools, and puts forward the solutions to the problem.
文摘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.
基金supported by the National Natural Science Foundation of China(Grant No.41961134032).
文摘The surrogate model serves as an efficient simulation tool during the slope parameter inversion process.However,the creep constitutive model integrated with dynamic damage evolution poses challenges in development of the required surrogate model.In this study,a novel physics knowledge-based surrogate model framework is proposed.In this framework,a Transformer module is employed to capture straindriven softening-hardening physical mechanisms.Positional encoding and self-attention are utilized to transform the constitutive parameters associated with shear strain,which are not directly time-related,into intermediate latent features for physical loss calculation.Next,a multi-layer stacked GRU(gated recurrent unit)network is built to provide input interfaces for time-dependent intermediate latent features,hydraulic boundary conditions,and water-rock interaction degradation equations,with static parameters introduced via external fully-connected layers.Finally,a combined loss function is constructed to facilitate the collaborative training of physical and data loss,introducing time-dependent weight adjustments to focus the surrogate model on accurate deformation predictions during critical phases.Based on the deformation of a reservoir bank landslide triggered by impoundment and subsequent restabilization,an elasto-viscoplastic constitutive model that considers water effect and sliding state dependencies is developed to validate the proposed surrogate model framework.The results indicate that the framework exhibits good performance in capturing physical mechanisms and predicting creep behavior,reducing errors by about 30 times compared to baseline models such as GRU and LSTM(long short-term memory),meeting the precision requirements for parameter inversion.Ablation experiments also confirmed the effectiveness of the framework.This framework can also serve as a reference for constructing other creep surrogate models that involve non-time-related across dimensions.
基金supported in part by the National Science and Technology Major Project of China(No.2019-I-0019-0018)the National Natural Science Foundation of China(Nos.61890920,61890921,12302065 and 12172073).
文摘Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.
文摘L1 and L2 acquisition, in some respects, are similar. Language development in children goes hand in hand with physical and cognitive development. Children learn their first language by imitation, but not always and not only by imitation. There seems to be some "innate capacities" that make children start to speak at the same time they do and in the way they do it. Adults learning a second language usually are controlled more by their motivation. But language input is important for both L1 and L2 acquisition. Though there are differences between CL1 and between CL2 and AL2, the way in which these learners acquire some of the grammatical morphemes is similar. This, together with some other evidence, shows that it is not only children who can acquire language. Adults can also acquire a language. But when adults acquire a language, they should also learn it. Some of the ways in which children acquire their language can be used as a model for L2 acquisition, even for Chinese students whose language is unrelated to English and whose culture is different. Learning the culture of the English-speaking countries will benefit the learning of the language. Like children, listening should also be well in advance of speaking in L2 acquisition. To train listening comprehension skills, Asher’s TPR approach proves more effective. TPR approach is at the moment limited to the beginning stage only. In order for students to gain all the five skills in a second language learning, namely, listening, speaking, reading, writing, and interpreting/translating, other methods should be used at the same time, or at later stages.
基金supported by an internal research grant from the School of Sport,Leeds Beckett University
文摘Purpose:To identify co-produced multi-stakeholder perspectives important for successful widespread physically active learning(PAL) adoption and implementation.Methods:A total of 35 stakeholders(policymakers n=9;commercial education sector,n=8;teachers,n=3;researchers,n=15) attended a design thinking PAL workshop.Participants formed 5 multi-disciplinary groups with at least 1 representative from each stakeholder group.Each group,facilitated by a researcher,undertook 2 tasks:(1) using Post-it Notes,the following question was answered:within the school day,what are the opportunities for learning combined with movement?and(2) structured as a washing-line task,the following question was answered:how can we establish PAL as the norm?All discussions were audio-recorded and transcribed.Inductive analyses were conducted by 4 authors.After the analyses were complete,the main themes and subthemes were assigned to 4 predetermined categories:(1) PAL design and implementation,(2) priorities for practice,(3) priorities for policy,and(4) priorities for research.Results:The following were the main themes for PAL implementation:opportunities for PAL within the school day,delivery environments,learning approaches,and the intensity of PAL.The main themes for the priorities for practice included teacher confidence and competence,resources to support delivery,and community of practice.The main themes for the policy for priorities included self-governance,the Office for Standards in Education,Children’s Services,and Skill,policy investment in initial teacher training,and curriculum reform.The main themes for the research priorities included establishing a strong evidence base,school-based PAL implementation,and a whole-systems approach.Conclusion:The present study is the first to identify PAL implementation factors using a combined multi-stakeholder perspective.To achieve wider PAL adoption and implementation,future interventions should be evidence based and address implementation factors at the classroom level(e.g.,approaches and delivery environments),school level(e.g.,communities of practice),and policy level(e.g.,initial teacher training).
基金RGC(ECS 24203724,YCRG C4004-24Y,YCRG C1002-22Y)NSFC(62405258)+2 种基金Innovation and Technology Fund(ITS/237/22)Innovation and TechnologyFund(ITS/226/21FP)NSFC/RGC Joint Research Scheme(N_CUHK444/22).
文摘Driven by advancements in artificial intelligence,end-to-end learning has become a key method for system optimization in various fields,including communications.However,applying learning algorithms such as backpropagation directly to communication systems is challenging due to their non-differentiable nature.Existing methods typically require developing a precise differentiable digital model of the physical system,which is computationally complex and can cause significant performance loss after deployment.In response,we propose a novel end-to-end learning framework called physics-guided learning.
基金supported by the National Key Research and Development Program of China(Grant Nos.2022YFA1403300,and 2020YFA0309100)the National Natural Science Foundation of China(Grant Nos.12204107,and 12074073)+2 种基金Shanghai Municipal Science and Technology Major Project(Grant No.2019SHZDZX01)Shanghai Pujiang Program(Grant No.21PJ1401500)Shanghai Science and Technology Committee(Grant Nos.21JC1406200,and 20JC1415900)。
文摘Physical neural networks are artificial neural networks that mimic synapses and neurons using physical systems or materials.These networks harness the distinctive characteristics of physical systems to carry out computations effectively,potentially surpassing the constraints of conventional digital neural networks.A recent advancement known as“physical self-learning”aims to achieve learning through intrinsic physical processes rather than relying on external computations.This article offers a comprehensive review of the progress made in implementing physical self-learning across various physical systems.Prevailing learning strategies that contribute to the realization of physical self-learning are discussed.Despite challenges in understanding the fundamental mechanism of learning,this work highlights the progress towards constructing intelligent hardware from the ground up,incorporating embedded self-organizing and self-adaptive dynamics in physical systems.
基金Supported by the National Science Foundation of USA ( the most recent one being num bered PHY- 970 4 5 2 0 ) and by the U niversities of Missouri and Nebraska ( U SA )
基金supported in part by NSF grant DMS-1522585partly sponsored by the Ralph N.Read Endowment of the Georgia Institute of Technology.
文摘A novel neural network method is developed for solving systems of conservation laws whose solutions may contain abrupt changes of state,including shock waves and contact discontinuities.In conventional approaches,a low-cost solution patch is usually used as the input to a neural network for predicting the high-fidelity solution patch.With that technique,however,there is no way to distinguish a smeared discontinuity from a smooth solution with large gradient in the input,and the two almost identical inputs correspond to two fundamentally different high-fidelity solution patches in training and predicting.To circumvent this difficulty,we use local patches of two low-cost numerical solutions of the conservation laws in a converging sequence as the input to a neural network.The neural network then makes a correct prediction by identifying whether the solution contains discontinuities or just smooth variations with large gradients,because the former becomes increasingly steep in a converging sequence in the input,and the latter does not.The inputs can be computed from lowcost numerical schemes with coarse resolution,in a local domain of dependence of a space-time location where the prediction is to be made.Despite smeared input solutions,the output provides sharp approximations of solutions containing shock waves and contact discontinuities.The method works effectively not only for regions with discontinuities,but also for smooth regions of the solution.It is efficient to implement,once trained,and has broader applications for different types of differential equations.
文摘In our prior work[10],neural networks with local converging inputs(NNLCI)were introduced for solving one-dimensional conservation equations.Two solutions of a conservation law in a converging sequence,computed from low-cost numerical schemes,and in a local domain of dependence of the space-time location,were used as the input to a neural network in order to predict a high-fidelity solution at a given space-time location.In the present work,we extend the method to twodimensional conservation systems and introduce different solution techniques.Numerical results demonstrate the validity and effectiveness of the NNLCI method for application to multi-dimensional problems.In spite of low-cost smeared input data,the NNLCI method is capable of accurately predicting shocks,contact discontinuities,and the smooth region of the entire field.The NNLCI method is relatively easy to train because of the use of local solvers.The computing time saving is between one and two orders of magnitude compared with the corresponding high-fidelity schemes for two-dimensional Riemann problems.The relative efficiency of the NNLCI method is expected to be substantially greater for problems with higher spatial dimensions or smooth solutions.
