Pub Date : 2023-10-13DOI: 10.1088/2632-2153/ad035d
Rafia Shaikh, Muhammad Rafi, Naeem Ahmed Ahmed Mahoto, Adel Sulaiman, Asadullah Shaikh
Abstract Multi–label classification is a fast–growing field of Machine Learning. Recent developments have shown several applications including social media, healthcare, bio–molecular analysis, scene, and music classification associated with the multilabel classification. In classification problems, instead of a single–label class assignment, multiple labels (multilabel or more than one class label) are assigned to an unseen record. Feature selection is a preprocessing phase used to identify the most relevant features that could improve the accuracy of the multilabel classifiers. The focus of this study is the feature selection method in multilabel classification. The
 study used a filter method in feature selection that involved the fisher score, ANOVA test, and mutual information. An extensive range of machine learning algorithms is applied in the modeling phase of a multilabel classification model that includes Binary Relevance, Classifier Chain, Label Powerset, Binary Relevance KNN, Multi–label Twin Support Vector Machine (MLTSVM), Multi–label KNN(MLKNN). Besides, label space partitioning and majority voting of ensemble methods are used, and also Random Forest as base learner. The experiments are carried out over five different multilabel benchmarking datasets. The evaluation of the classification model is measured using accuracy, precision, recall, F1 score, and hamming loss. The study demonstrated that the filter methods (i.e., mutual information) having top weighted 80% to 20% features provided significant outcomes.
{"title":"A Filter–based Feature Selection Approach in Multilabel Classification","authors":"Rafia Shaikh, Muhammad Rafi, Naeem Ahmed Ahmed Mahoto, Adel Sulaiman, Asadullah Shaikh","doi":"10.1088/2632-2153/ad035d","DOIUrl":"https://doi.org/10.1088/2632-2153/ad035d","url":null,"abstract":"Abstract Multi–label classification is a fast–growing field of Machine Learning. Recent developments have shown several applications including social media, healthcare, bio–molecular analysis, scene, and music classification associated with the multilabel classification. In classification problems, instead of a single–label class assignment, multiple labels (multilabel or more than one class label) are assigned to an unseen record. Feature selection is a preprocessing phase used to identify the most relevant features that could improve the accuracy of the multilabel classifiers. The focus of this study is the feature selection method in multilabel classification. The
 study used a filter method in feature selection that involved the fisher score, ANOVA test, and mutual information. An extensive range of machine learning algorithms is applied in the modeling phase of a multilabel classification model that includes Binary Relevance, Classifier Chain, Label Powerset, Binary Relevance KNN, Multi–label Twin Support Vector Machine (MLTSVM), Multi–label KNN(MLKNN). Besides, label space partitioning and majority voting of ensemble methods are used, and also Random Forest as base learner. The experiments are carried out over five different multilabel benchmarking datasets. The evaluation of the classification model is measured using accuracy, precision, recall, F1 score, and hamming loss. The study demonstrated that the filter methods (i.e., mutual information) having top weighted 80% to 20% features provided significant outcomes.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135853616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1088/2632-2153/acffa4
Dongni Jia, Xiaofeng Zhou, Shuai Li, Shurui Liu, Haibo Shi
Abstract The governing equations of nonlinear dynamic systems is of great significance for understanding the internal physical characteristics. In order to learn the governing equations of nonlinear systems from noisy observed data, we propose a novel method named governing equation discovery based on causal graph that combines spatio-temporal graph convolution network with governing equation modeling. The essence of our method is to first devise the causal graph encoding based on transfer entropy to obtain the adjacency matrix with causal significance between variables. Then, the spatio-temporal graph convolutional network is used to obtain approximate solutions for the system variables. On this basis, automatic differentiation is applied to obtain basic derivatives and form a dictionary of candidate algebraic terms. Finally, sparse regression is used to obtain the coefficient matrix and determine the explicit formulation of the governing equations. We also design a novel cross-combinatorial optimization strategy to learn the heterogeneous parameters that include neural network parameters and control equation coefficients. We conduct extensive experiments on seven datasets from different physical fields. The experimental results demonstrate the proposed method can automatically discover the underlying governing equation of the systems, and has great robustness.
{"title":"Governing equation discovery based on causal graph for nonlinear dynamic systems","authors":"Dongni Jia, Xiaofeng Zhou, Shuai Li, Shurui Liu, Haibo Shi","doi":"10.1088/2632-2153/acffa4","DOIUrl":"https://doi.org/10.1088/2632-2153/acffa4","url":null,"abstract":"Abstract The governing equations of nonlinear dynamic systems is of great significance for understanding the internal physical characteristics. In order to learn the governing equations of nonlinear systems from noisy observed data, we propose a novel method named governing equation discovery based on causal graph that combines spatio-temporal graph convolution network with governing equation modeling. The essence of our method is to first devise the causal graph encoding based on transfer entropy to obtain the adjacency matrix with causal significance between variables. Then, the spatio-temporal graph convolutional network is used to obtain approximate solutions for the system variables. On this basis, automatic differentiation is applied to obtain basic derivatives and form a dictionary of candidate algebraic terms. Finally, sparse regression is used to obtain the coefficient matrix and determine the explicit formulation of the governing equations. We also design a novel cross-combinatorial optimization strategy to learn the heterogeneous parameters that include neural network parameters and control equation coefficients. We conduct extensive experiments on seven datasets from different physical fields. The experimental results demonstrate the proposed method can automatically discover the underlying governing equation of the systems, and has great robustness.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135804909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1088/2632-2153/ad035b
Nathalie Soybelman, Nilotpal Kakati, Lukas Heinrich, Francesco Armando Di Bello, Etienne Dreyer, Sanmay Ganguly, Eilam Gross, Marumi Kado, Jonathan Shlomi
Abstract The simulation of particle physics data is a fundamental but computationally
intensive ingredient for physics analysis at the Large Hadron Collider, where observational
set-valued data is generated conditional on a set of incoming particles. To accelerate this
task, we present a novel generative model based on a graph neural network and slot-attention
components, which exceeds the performance of pre-existing baselines.
{"title":"Set-Conditional Set Generation for Particle Physics","authors":"Nathalie Soybelman, Nilotpal Kakati, Lukas Heinrich, Francesco Armando Di Bello, Etienne Dreyer, Sanmay Ganguly, Eilam Gross, Marumi Kado, Jonathan Shlomi","doi":"10.1088/2632-2153/ad035b","DOIUrl":"https://doi.org/10.1088/2632-2153/ad035b","url":null,"abstract":"Abstract The simulation of particle physics data is a fundamental but computationally
intensive ingredient for physics analysis at the Large Hadron Collider, where observational
set-valued data is generated conditional on a set of incoming particles. To accelerate this
task, we present a novel generative model based on a graph neural network and slot-attention
components, which exceeds the performance of pre-existing baselines.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135854209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1088/2632-2153/acffa3
Yun-Zhong Qiu
Abstract Quantum adversarial machine learning is an emerging field that studies the vulnerability of quantum learning systems against adversarial perturbations and develops possible defense strategies. Quantum universal adversarial perturbations are small perturbations, which can make different input samples into adversarial examples that may deceive a given quantum classifier. This is a field that was rarely looked into but worthwhile investigating because universal perturbations might simplify malicious attacks to a large extent, causing unexpected devastation to quantum machine learning models. In this paper, we take a step forward and explore the quantum universal perturbations in the context of heterogeneous classification tasks. In particular, we find that quantum classifiers that achieve almost state-of-the-art accuracy on two different classification tasks can be both conclusively deceived by one carefully-crafted universal perturbation. This result is explicitly demonstrated with well-designed quantum continual learning models with elastic weight consolidation method to avoid catastrophic forgetting, as well as real-life heterogeneous datasets from hand-written digits and medical MRI images. Our results provide a simple and efficient way to generate universal perturbations on heterogeneous classification tasks and thus would provide valuable guidance for future quantum learning technologies.
{"title":"Universal adversarial perturbations for multiple classification tasks with quantum classifiers","authors":"Yun-Zhong Qiu","doi":"10.1088/2632-2153/acffa3","DOIUrl":"https://doi.org/10.1088/2632-2153/acffa3","url":null,"abstract":"Abstract Quantum adversarial machine learning is an emerging field that studies the vulnerability of quantum learning systems against adversarial perturbations and develops possible defense strategies. Quantum universal adversarial perturbations are small perturbations, which can make different input samples into adversarial examples that may deceive a given quantum classifier. This is a field that was rarely looked into but worthwhile investigating because universal perturbations might simplify malicious attacks to a large extent, causing unexpected devastation to quantum machine learning models. In this paper, we take a step forward and explore the quantum universal perturbations in the context of heterogeneous classification tasks. In particular, we find that quantum classifiers that achieve almost state-of-the-art accuracy on two different classification tasks can be both conclusively deceived by one carefully-crafted universal perturbation. This result is explicitly demonstrated with well-designed quantum continual learning models with elastic weight consolidation method to avoid catastrophic forgetting, as well as real-life heterogeneous datasets from hand-written digits and medical MRI images. Our results provide a simple and efficient way to generate universal perturbations on heterogeneous classification tasks and thus would provide valuable guidance for future quantum learning technologies.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135804737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1088/2632-2153/acfd08
Sujin Seol, Jungeun Lee, Jaewoo Yoon, Byeongwoo Kim
Abstract Recently, the xEV market has been expanding by strengthening regulations on fossil fuel vehicles. It is essential to ensure the safety and reliability of batteries, one of the core components of xEVs. Furthermore, estimating the battery’s state of health (SOH) is critical. There are model-based and data-based methods for SOH estimation. Model-based methods have limitations in linearly modeling the nonlinear internal state changes of batteries. In data-based methods, high-quality datasets containing large quantities of data are crucial. Since obtaining battery datasets through measurement is difficult, this paper supplements insufficient battery datasets using time-series generative adversarial network and compares the improvement rate in SOH estimation accuracy through long short-term memory and gated recurrent unit based on recurrent neural networks. According to the results, the average root mean square error of battery SOH estimation improved by approximately 25%, and the learning stability improved by approximately 40%.
{"title":"Improving SOH estimation for lithium-ion batteries using TimeGAN","authors":"Sujin Seol, Jungeun Lee, Jaewoo Yoon, Byeongwoo Kim","doi":"10.1088/2632-2153/acfd08","DOIUrl":"https://doi.org/10.1088/2632-2153/acfd08","url":null,"abstract":"Abstract Recently, the xEV market has been expanding by strengthening regulations on fossil fuel vehicles. It is essential to ensure the safety and reliability of batteries, one of the core components of xEVs. Furthermore, estimating the battery’s state of health (SOH) is critical. There are model-based and data-based methods for SOH estimation. Model-based methods have limitations in linearly modeling the nonlinear internal state changes of batteries. In data-based methods, high-quality datasets containing large quantities of data are crucial. Since obtaining battery datasets through measurement is difficult, this paper supplements insufficient battery datasets using time-series generative adversarial network and compares the improvement rate in SOH estimation accuracy through long short-term memory and gated recurrent unit based on recurrent neural networks. According to the results, the average root mean square error of battery SOH estimation improved by approximately 25%, and the learning stability improved by approximately 40%.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135923427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.1088/2632-2153/acffa2
Yu Miyazaki
Abstract I present a novel equivariant neural network architecture for the large-scale spin dynamics simulation of the Kondo lattice model. This neural network mainly consists of tensor-product-based convolution layers and ensures two equivariances: translations of the lattice and rotations of the spins. I implement equivariant neural networks for two Kondo lattice models on two-dimensional square and triangular lattices, and perform training and validation. In the equivariant model for the square lattice, the validation error (based on root mean squared error) is reduced to less than one-third compared to a model using invariant descriptors as inputs. Furthermore, I demonstrate the ability to simulate phase transitions of skyrmion crystals in the triangular lattice, by performing dynamics simulations using the trained model.
{"title":"Equivariant Neural Networks for Spin Dynamics Simulations of Itinerant Magnets","authors":"Yu Miyazaki","doi":"10.1088/2632-2153/acffa2","DOIUrl":"https://doi.org/10.1088/2632-2153/acffa2","url":null,"abstract":"Abstract I present a novel equivariant neural network architecture for the large-scale spin dynamics simulation of the Kondo lattice model. This neural network mainly consists of tensor-product-based convolution layers and ensures two equivariances: translations of the lattice and rotations of the spins. I implement equivariant neural networks for two Kondo lattice models on two-dimensional square and triangular lattices, and perform training and validation. In the equivariant model for the square lattice, the validation error (based on root mean squared error) is reduced to less than one-third compared to a model using invariant descriptors as inputs. Furthermore, I demonstrate the ability to simulate phase transitions of skyrmion crystals in the triangular lattice, by performing dynamics simulations using the trained model.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136057800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.1088/2632-2153/ad0285
Natália V. N. Rodrigues, L. Raul Abramo, Nina S. Hirata
Abstract Errors in measurements are key to weighting the value of data, but are often neglected in Machine Learning (ML). 
We show how Convolutional Neural Networks (CNNs) are able to learn about the context and patterns of signal and noise, leading to improvements in the performance of classification methods.
We construct a model whereby two classes of objects follow an underlying Gaussian distribution, and where the features (the input data) have varying, but known, levels of noise -- in other words, each data point has a different error bar.
This model mimics the nature of scientific data sets, such as those from astrophysical surveys, where noise arises as a realization of random processes with known underlying distributions.
The classification of these objects can then be performed using standard statistical techniques (e.g., least-squares minimization or Markov-Chain Monte Carlo), as well as ML techniques. 
This allows us to take advantage of a maximum likelihood approach to object classification, and to measure the amount by which the ML methods are incorporating the information in the input data uncertainties.
We show that, when each data point is subject to different levels of noise (i.e., noises with different distribution functions, which is typically the case in scientific data sets), that information can be learned by the CNNs, raising the ML performance to at least the same level of the least-squares method -- and sometimes even surpassing it.
Furthermore, we show that, with varying noise levels, the confidence of the ML classifiers serves as a proxy for the underlying cumulative distribution function, but only if the information about specific input data uncertainties is provided to the CNNs.
{"title":"The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data","authors":"Natália V. N. Rodrigues, L. Raul Abramo, Nina S. Hirata","doi":"10.1088/2632-2153/ad0285","DOIUrl":"https://doi.org/10.1088/2632-2153/ad0285","url":null,"abstract":"Abstract Errors in measurements are key to weighting the value of data, but are often neglected in Machine Learning (ML). 
We show how Convolutional Neural Networks (CNNs) are able to learn about the context and patterns of signal and noise, leading to improvements in the performance of classification methods.
We construct a model whereby two classes of objects follow an underlying Gaussian distribution, and where the features (the input data) have varying, but known, levels of noise -- in other words, each data point has a different error bar.
This model mimics the nature of scientific data sets, such as those from astrophysical surveys, where noise arises as a realization of random processes with known underlying distributions.
The classification of these objects can then be performed using standard statistical techniques (e.g., least-squares minimization or Markov-Chain Monte Carlo), as well as ML techniques. 
This allows us to take advantage of a maximum likelihood approach to object classification, and to measure the amount by which the ML methods are incorporating the information in the input data uncertainties.
We show that, when each data point is subject to different levels of noise (i.e., noises with different distribution functions, which is typically the case in scientific data sets), that information can be learned by the CNNs, raising the ML performance to at least the same level of the least-squares method -- and sometimes even surpassing it.
Furthermore, we show that, with varying noise levels, the confidence of the ML classifiers serves as a proxy for the underlying cumulative distribution function, but only if the information about specific input data uncertainties is provided to the CNNs.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136058236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-10DOI: 10.1088/2632-2153/ad020d
Leopoldo Sarra, Florian Marquardt
Abstract Bayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system by maximizing the expected information gain. Recent developments in deep neural networks and normalizing flows allow for a more efficient approximation of the posterior and thus the extension of this technique to complex high-dimensional situations. In this paper, we show how this approach holds promise for adaptive measurement strategies to characterize present-day quantum technology platforms. In particular, we focus on arrays of coupled cavities and qubit arrays. Both represent model systems of high relevance for modern applications, like quantum simulations and computing, and both have been realized in platforms where measurement and control can be exploited to characterize and counteract unavoidable disorder. Thus, they represent ideal targets for applications of Bayesian experimental design.
{"title":"Deep Bayesian Experimental Design for Quantum Many-Body systems","authors":"Leopoldo Sarra, Florian Marquardt","doi":"10.1088/2632-2153/ad020d","DOIUrl":"https://doi.org/10.1088/2632-2153/ad020d","url":null,"abstract":"Abstract Bayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system by maximizing the expected information gain. Recent developments in deep neural networks and normalizing flows allow for a more efficient approximation of the posterior and thus the extension of this technique to complex high-dimensional situations. In this paper, we show how this approach holds promise for adaptive measurement strategies to characterize present-day quantum technology platforms. In particular, we focus on arrays of coupled cavities and qubit arrays. Both represent model systems of high relevance for modern applications, like quantum simulations and computing, and both have been realized in platforms where measurement and control can be exploited to characterize and counteract unavoidable disorder. Thus, they represent ideal targets for applications of Bayesian experimental design.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136292352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-09DOI: 10.1088/2632-2153/acfa63
Pablo Lemos, Niall Jeffrey, Miles Cranmer, Shirley Ho, Peter Battaglia
Abstract We present an approach for using machine learning to automatically discover the governing equations and unknown properties (in this case, masses) of real physical systems from observations. We train a ‘graph neural network’ to simulate the dynamics of our Solar System’s Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to correctly infer an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton’s law of gravitation. The key assumptions our method makes are translational and rotational equivariance, and Newton’s second and third laws of motion. It did not, however, require any assumptions about the masses of planets and moons or physical constants, but nonetheless, they, too, were accurately inferred with our method. Naturally, the classical law of gravitation has been known since Isaac Newton, but our results demonstrate that our method can discover unknown laws and hidden properties from observed data.
{"title":"Rediscovering orbital mechanics with machine learning","authors":"Pablo Lemos, Niall Jeffrey, Miles Cranmer, Shirley Ho, Peter Battaglia","doi":"10.1088/2632-2153/acfa63","DOIUrl":"https://doi.org/10.1088/2632-2153/acfa63","url":null,"abstract":"Abstract We present an approach for using machine learning to automatically discover the governing equations and unknown properties (in this case, masses) of real physical systems from observations. We train a ‘graph neural network’ to simulate the dynamics of our Solar System’s Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to correctly infer an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton’s law of gravitation. The key assumptions our method makes are translational and rotational equivariance, and Newton’s second and third laws of motion. It did not, however, require any assumptions about the masses of planets and moons or physical constants, but nonetheless, they, too, were accurately inferred with our method. Naturally, the classical law of gravitation has been known since Isaac Newton, but our results demonstrate that our method can discover unknown laws and hidden properties from observed data.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135043454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-09DOI: 10.1088/2632-2153/acf6a9
Arpan Biswas, Maxim Ziatdinov, Sergei V. Kalinin
Abstract Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as electron energy loss spectroscopy or 4D scanning transmission electron microscope, that contain information on a wide range of structural, physical, and chemical properties of materials. To extract valuable insights from these data, it is crucial to identify physically separate regions in the data, such as phases, ferroic variants, and boundaries between them. In order to derive an easily interpretable feature analysis, combining with well-defined boundaries in a principled and unsupervised manner, here we present a physics augmented machine learning method which combines the capability of variational autoencoders to disentangle factors of variability within the data and the physics driven loss function that seeks to minimize the total length of the discontinuities in images corresponding to latent representations. Our method is applied to various materials, including NiO-LSMO, BiFeO 3 , and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data. The customized codes of the required functions and classes to develop phyVAE is available at https://github.com/arpanbiswas52/phy-VAE .
{"title":"Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis","authors":"Arpan Biswas, Maxim Ziatdinov, Sergei V. Kalinin","doi":"10.1088/2632-2153/acf6a9","DOIUrl":"https://doi.org/10.1088/2632-2153/acf6a9","url":null,"abstract":"Abstract Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as electron energy loss spectroscopy or 4D scanning transmission electron microscope, that contain information on a wide range of structural, physical, and chemical properties of materials. To extract valuable insights from these data, it is crucial to identify physically separate regions in the data, such as phases, ferroic variants, and boundaries between them. In order to derive an easily interpretable feature analysis, combining with well-defined boundaries in a principled and unsupervised manner, here we present a physics augmented machine learning method which combines the capability of variational autoencoders to disentangle factors of variability within the data and the physics driven loss function that seeks to minimize the total length of the discontinuities in images corresponding to latent representations. Our method is applied to various materials, including NiO-LSMO, BiFeO 3 , and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data. The customized codes of the required functions and classes to develop phyVAE is available at https://github.com/arpanbiswas52/phy-VAE .","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135043752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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