Pub Date : 2024-03-20DOI: 10.1088/2632-2153/ad360e
Yun-Wen Mao, R. Krems
We demonstrate accurate data-starved models of molecular properties for interpolation in chemical compound spaces with low-dimensional descriptors. Our starting point is based on three-dimensional, universal, physical descriptors derived from the properties of the distributions of the eigenvalues of Coulomb matrices. To account for the shape and composition of molecules, we combine these descriptors with six-dimensional features informed by the Gershgorin circle theorem. We use the nine-dimensional descriptors thus obtained for Gaussian process regression based on kernels with variable functional form, leading to extremely efficient, low-dimensional interpolation models. The resulting models trained with 100 molecules are able to predict the product of entropy and temperature (S × T ) and zero point vibrational energy (ZPVE) with the absolute error under 1 kcal/mol for > 78 % and under 1.3 kcal/mol for > 92 % of molecules in the test data. The test data comprises 20,000 molecules with complexity varying from three atoms to 29 atoms and the ranges of S × T and ZPVE covering 36 kcal/mol and 161 kcal/mol, respectively. We also illustrate that the descriptors based on the Gershgorin circle theorem yield more accurate models of molecular entropy than those based on graph neural networks that explicitly account for the atomic connectivity of molecules.
{"title":"Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors","authors":"Yun-Wen Mao, R. Krems","doi":"10.1088/2632-2153/ad360e","DOIUrl":"https://doi.org/10.1088/2632-2153/ad360e","url":null,"abstract":"\u0000 We demonstrate accurate data-starved models of molecular properties for interpolation in chemical compound spaces with low-dimensional descriptors. Our starting point is based on three-dimensional, universal, physical descriptors derived from the properties of the distributions of the eigenvalues of Coulomb matrices. To account for the shape and composition of molecules, we combine these descriptors with six-dimensional features informed by the Gershgorin circle theorem. We use the nine-dimensional descriptors thus obtained for Gaussian process regression based on kernels with variable functional form, leading to extremely efficient, low-dimensional interpolation models. The resulting models trained with 100 molecules are able to predict the product of entropy and temperature (S × T ) and zero point vibrational energy (ZPVE) with the absolute error under 1 kcal/mol for > 78 % and under 1.3 kcal/mol for > 92 % of molecules in the test data. The test data comprises 20,000 molecules with complexity varying from three atoms to 29 atoms and the ranges of S × T and ZPVE covering 36 kcal/mol and 161 kcal/mol, respectively. We also illustrate that the descriptors based on the Gershgorin circle theorem yield more accurate models of molecular entropy than those based on graph neural networks that explicitly account for the atomic connectivity of molecules.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"5 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140227658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-19DOI: 10.1088/2632-2153/ad35a3
Junyu Liu, Zexi Lin, L. Jiang
We define emph{laziness} to describe a large suppression of variational parameter updates for neural networks, classical or quantum. In the quantum case, the suppression is exponential in the number of qubits for randomized variational quantum circuits. We discuss the difference between laziness and emph{barren plateau} in quantum machine learning created by quantum physicists in cite{mcclean2018barren} for the flatness of the loss function landscape during gradient descent. We address a novel theoretical understanding of those two phenomena in light of the theory of neural tangent kernels. For noiseless quantum circuits, without the measurement noise, the loss function landscape is complicated in the overparametrized regime with a large number of trainable variational angles. Instead, around a random starting point in optimization, there are large numbers of local minima that are good enough and could minimize the mean square loss function, where we still have quantum laziness, but we do not have barren plateaus. However, the complicated landscape is not visible within a limited number of iterations, and low precision in quantum control and quantum sensing. Moreover, we look at the effect of noises during optimization by assuming intuitive noise models, and show that variational quantum algorithms are noise-resilient in the overparametrization regime. Our work precisely reformulates the quantum barren plateau statement towards a precision statement and justifies the statement in certain noise models, injects new hope toward near-term variational quantum algorithms, and provides theoretical connections toward classical machine learning.
{"title":"Laziness, Barren Plateau, and Noises in Machine Learning","authors":"Junyu Liu, Zexi Lin, L. Jiang","doi":"10.1088/2632-2153/ad35a3","DOIUrl":"https://doi.org/10.1088/2632-2153/ad35a3","url":null,"abstract":"\u0000 We define emph{laziness} to describe a large suppression of variational parameter updates for neural networks, classical or quantum. In the quantum case, the suppression is exponential in the number of qubits for randomized variational quantum circuits. We discuss the difference between laziness and emph{barren plateau} in quantum machine learning created by quantum physicists in cite{mcclean2018barren} for the flatness of the loss function landscape during gradient descent. We address a novel theoretical understanding of those two phenomena in light of the theory of neural tangent kernels. For noiseless quantum circuits, without the measurement noise, the loss function landscape is complicated in the overparametrized regime with a large number of trainable variational angles. Instead, around a random starting point in optimization, there are large numbers of local minima that are good enough and could minimize the mean square loss function, where we still have quantum laziness, but we do not have barren plateaus. However, the complicated landscape is not visible within a limited number of iterations, and low precision in quantum control and quantum sensing. Moreover, we look at the effect of noises during optimization by assuming intuitive noise models, and show that variational quantum algorithms are noise-resilient in the overparametrization regime. Our work precisely reformulates the quantum barren plateau statement towards a precision statement and justifies the statement in certain noise models, injects new hope toward near-term variational quantum algorithms, and provides theoretical connections toward classical machine learning.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"62 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-14DOI: 10.1088/2632-2153/ad33e1
Juan José García-Esteban, Juan Carlos Cuevas, Jorge Bravo-Abad
Generative adversarial networks (GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic spectral data generation for data-scarce radiative heat transfer applications, an area where their use has not been previously reported. We demonstrate the proposed approach by applying it to an illustrative problem within the realm of near-field radiative heat transfer involving a multilayered hyperbolic metamaterial. We find that a successful generation of spectral data requires two modifications to conventional GANs: (i) the introduction of Wasserstein GANs (WGANs) to avoid mode collapse, and, (ii) the conditioning of WGANs to obtain accurate labels for the generated data. We show that a simple feed-forward neural network (FFNN), when augmented with data generated by a CWGAN, enhances significantly its performance under conditions of limited data availability. In addition, we show that CWGANs can act as a surrogate model with improved performance in the low-data regime with respect to simple FFNNs. Overall, this work contributes to highlight the potential of generative machine learning algorithms in scientific applications beyond image generation and optimization.
{"title":"Generative adversarial networks for data-scarce radiative heat transfer applications","authors":"Juan José García-Esteban, Juan Carlos Cuevas, Jorge Bravo-Abad","doi":"10.1088/2632-2153/ad33e1","DOIUrl":"https://doi.org/10.1088/2632-2153/ad33e1","url":null,"abstract":"\u0000 Generative adversarial networks (GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic spectral data generation for data-scarce radiative heat transfer applications, an area where their use has not been previously reported. We demonstrate the proposed approach by applying it to an illustrative problem within the realm of near-field radiative heat transfer involving a multilayered hyperbolic metamaterial. We find that a successful generation of spectral data requires two modifications to conventional GANs: (i) the introduction of Wasserstein GANs (WGANs) to avoid mode collapse, and, (ii) the conditioning of WGANs to obtain accurate labels for the generated data. We show that a simple feed-forward neural network (FFNN), when augmented with data generated by a CWGAN, enhances significantly its performance under conditions of limited data availability. In addition, we show that CWGANs can act as a surrogate model with improved performance in the low-data regime with respect to simple FFNNs. Overall, this work contributes to highlight the potential of generative machine learning algorithms in scientific applications beyond image generation and optimization.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-14DOI: 10.1088/2632-2153/ad33df
J. F. García-Samartín, Christyan Cruz, Jaime del Cerro, Antonio Barrientos
In recent years, legged quadruped robots have proved to be a valuable support to humans in dealing with Search and Rescue (SAR) operations. These robots can move with great ability in complex terrains, unstructured environments or regions with many obstacles. This work employs the quadruped robot ARTU-R (A1 Rescue Tasks UPM Robot) by Unitree, equipped with an RGB-D camera and a lidar, to perform victim searches in post-disaster scenarios. Exploration is done not by following a pre-planned path (as common methods) but by prioritising the areas most likely to harbour victims. To accomplish that task, both Indirect Search (IS) and Next Best View (NBV) techniques have been used. When ARTU-R gets inside an unstructured and unknown environment, it selects the next exploration point from a series of candidates. This operation is performed by comparing, for each candidate, the distance to reach it, the unexplored space around it and the probability of a victim being in its vicinity. This probability value is obtained using a Random Forest, which processes the information provided by a Convolutional Neural Network (CNN). Unlike other AI techniques, random forests are not black box models; humans can understand their decision-making processes. The system, once integrated, achieves speeds comparable to other state-of-the-art algorithms in terms of exploration, but concerning victim detection, the tests show that the resulting smart exploration generates logical paths --from a human point of view-- and that ARTU-R tends to move first to the regions where victims are present.
{"title":"Active Robotic Search for Victims using Ensemble Deep Learning Techniques","authors":"J. F. García-Samartín, Christyan Cruz, Jaime del Cerro, Antonio Barrientos","doi":"10.1088/2632-2153/ad33df","DOIUrl":"https://doi.org/10.1088/2632-2153/ad33df","url":null,"abstract":"\u0000 In recent years, legged quadruped robots have proved to be a valuable support to humans in dealing with Search and Rescue (SAR) operations. These robots can move with great ability in complex terrains, unstructured environments or regions with many obstacles. This work employs the quadruped robot ARTU-R (A1 Rescue Tasks UPM Robot) by Unitree, equipped with an RGB-D camera and a lidar, to perform victim searches in post-disaster scenarios. Exploration is done not by following a pre-planned path (as common methods) but by prioritising the areas most likely to harbour victims. To accomplish that task, both Indirect Search (IS) and Next Best View (NBV) techniques have been used. When ARTU-R gets inside an unstructured and unknown environment, it selects the next exploration point from a series of candidates. This operation is performed by comparing, for each candidate, the distance to reach it, the unexplored space around it and the probability of a victim being in its vicinity. This probability value is obtained using a Random Forest, which processes the information provided by a Convolutional Neural Network (CNN). Unlike other AI techniques, random forests are not black box models; humans can understand their decision-making processes. The system, once integrated, achieves speeds comparable to other state-of-the-art algorithms in terms of exploration, but concerning victim detection, the tests show that the resulting smart exploration generates logical paths --from a human point of view-- and that ARTU-R tends to move first to the regions where victims are present.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"27 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140243960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-02DOI: 10.1088/2632-2153/ad2f53
Tom Kirstein, S. Aßmann, O. Furat, Stefan Will, Volker Schmidt
Wide-angle light scattering (WALS) offers the possibility of a highly temporally and spatially resolved measurement of droplets in spray-based methods for nanoparticle synthesis. The size of these droplets is a critical variable affecting the final properties of synthesized materials such as hetero-aggregates. However, conventional methods for determining droplet sizes from WALS image data are labor-intensive and may introduce biases, particularly when applied to complex systems like spray flame synthesis (SFS). To address these challenges, we introduce a fully automatic machine learning-based approach that employs convolutional neural networks (CNNs) in order to streamline the droplet sizing process. This CNN-based methodology offers further advantages: it requires few manual labels and can utilize transfer learning, making it a promising alternative to conventional methods, specifically with respect to efficiency. To evaluate the performance of our machine learning models, we consider WALS data from an ethanol spray flame process at various heights above the burner surface (HABs), where the models are trained and cross-validated on a large dataset comprising nearly 35000 WALS images.
{"title":"Determination of droplet size from wide-angle light scattering image data using convolutional neural networks","authors":"Tom Kirstein, S. Aßmann, O. Furat, Stefan Will, Volker Schmidt","doi":"10.1088/2632-2153/ad2f53","DOIUrl":"https://doi.org/10.1088/2632-2153/ad2f53","url":null,"abstract":"\u0000 Wide-angle light scattering (WALS) offers the possibility of a highly temporally and spatially resolved measurement of droplets in spray-based methods for nanoparticle synthesis. The size of these droplets is a critical variable affecting the final properties of synthesized materials such as hetero-aggregates. However, conventional methods for determining droplet sizes from WALS image data are labor-intensive and may introduce biases, particularly when applied to complex systems like spray flame synthesis (SFS). To address these challenges, we introduce a fully automatic machine learning-based approach that employs convolutional neural networks (CNNs) in order to streamline the droplet sizing process. This CNN-based methodology offers further advantages: it requires few manual labels and can utilize transfer learning, making it a promising alternative to conventional methods, specifically with respect to efficiency. To evaluate the performance of our machine learning models, we consider WALS data from an ethanol spray flame process at various heights above the burner surface (HABs), where the models are trained and cross-validated on a large dataset comprising nearly 35000 WALS images.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"35 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140081344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-14DOI: 10.1088/2632-2153/ad2973
Xueqin Sun, Yu Li, Yihong Li, SuKai Wang, Xuan Li, Ming Lu, Ping Chen
Monitoring the burning surface regression in ground static ignition tests is crucial for predicting the internal ballistic performance of solid rocket motors (SRMs). A previously proposed ultra-sparse computed tomography (CT) imaging method provides a possibility for real-time monitoring. However, sample shortages of SRMs highlights the need for monitoring accuracy, especially given the high cost associated with the design and development of SRM systems. Therefore, constructing datasets via regression simulations to compensate for SRM sample shortages is critical. To address this issue, we recommend adopting the level-set (LS) method to dynamically track the burning surface by solving partial differential equations (PDEs). The computational cost of numerical solution is prohibitive for scientific applications involving large-scale spatiotemporal domains. The physics-informed neural network (PINN) and neural operator have been used to accelerate the solution of PDE, showing satisfactory prediction performance and high computational efficiency. We designed a physics-guided network, named LS-PhyNet, that couples the potential physical mechanisms of burning surface regression into the deep learning framework. The proposed method is capable of encoding well-established traditional numerical discretization methods into the network architecture to leverage prior knowledge of underlying physics, thus providing the model with enhanced expressive power and interpretability. Experimental results prove that LS-PhyNet can better reproduce the burning surfaces obtained by numerical solution with only small data regimes, providing a new paradigm for real-time monitoring of burning surface regression transients during static ignition tests.
{"title":"Regression Transients Modeling of Solid Rocket Motor Burning Surfaces with Physics-guided Neural Network","authors":"Xueqin Sun, Yu Li, Yihong Li, SuKai Wang, Xuan Li, Ming Lu, Ping Chen","doi":"10.1088/2632-2153/ad2973","DOIUrl":"https://doi.org/10.1088/2632-2153/ad2973","url":null,"abstract":"\u0000 Monitoring the burning surface regression in ground static ignition tests is crucial for predicting the internal ballistic performance of solid rocket motors (SRMs). A previously proposed ultra-sparse computed tomography (CT) imaging method provides a possibility for real-time monitoring. However, sample shortages of SRMs highlights the need for monitoring accuracy, especially given the high cost associated with the design and development of SRM systems. Therefore, constructing datasets via regression simulations to compensate for SRM sample shortages is critical. To address this issue, we recommend adopting the level-set (LS) method to dynamically track the burning surface by solving partial differential equations (PDEs). The computational cost of numerical solution is prohibitive for scientific applications involving large-scale spatiotemporal domains. The physics-informed neural network (PINN) and neural operator have been used to accelerate the solution of PDE, showing satisfactory prediction performance and high computational efficiency. We designed a physics-guided network, named LS-PhyNet, that couples the potential physical mechanisms of burning surface regression into the deep learning framework. The proposed method is capable of encoding well-established traditional numerical discretization methods into the network architecture to leverage prior knowledge of underlying physics, thus providing the model with enhanced expressive power and interpretability. Experimental results prove that LS-PhyNet can better reproduce the burning surfaces obtained by numerical solution with only small data regimes, providing a new paradigm for real-time monitoring of burning surface regression transients during static ignition tests.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"34 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139779566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-14DOI: 10.1088/2632-2153/ad2973
Xueqin Sun, Yu Li, Yihong Li, SuKai Wang, Xuan Li, Ming Lu, Ping Chen
Monitoring the burning surface regression in ground static ignition tests is crucial for predicting the internal ballistic performance of solid rocket motors (SRMs). A previously proposed ultra-sparse computed tomography (CT) imaging method provides a possibility for real-time monitoring. However, sample shortages of SRMs highlights the need for monitoring accuracy, especially given the high cost associated with the design and development of SRM systems. Therefore, constructing datasets via regression simulations to compensate for SRM sample shortages is critical. To address this issue, we recommend adopting the level-set (LS) method to dynamically track the burning surface by solving partial differential equations (PDEs). The computational cost of numerical solution is prohibitive for scientific applications involving large-scale spatiotemporal domains. The physics-informed neural network (PINN) and neural operator have been used to accelerate the solution of PDE, showing satisfactory prediction performance and high computational efficiency. We designed a physics-guided network, named LS-PhyNet, that couples the potential physical mechanisms of burning surface regression into the deep learning framework. The proposed method is capable of encoding well-established traditional numerical discretization methods into the network architecture to leverage prior knowledge of underlying physics, thus providing the model with enhanced expressive power and interpretability. Experimental results prove that LS-PhyNet can better reproduce the burning surfaces obtained by numerical solution with only small data regimes, providing a new paradigm for real-time monitoring of burning surface regression transients during static ignition tests.
{"title":"Regression Transients Modeling of Solid Rocket Motor Burning Surfaces with Physics-guided Neural Network","authors":"Xueqin Sun, Yu Li, Yihong Li, SuKai Wang, Xuan Li, Ming Lu, Ping Chen","doi":"10.1088/2632-2153/ad2973","DOIUrl":"https://doi.org/10.1088/2632-2153/ad2973","url":null,"abstract":"\u0000 Monitoring the burning surface regression in ground static ignition tests is crucial for predicting the internal ballistic performance of solid rocket motors (SRMs). A previously proposed ultra-sparse computed tomography (CT) imaging method provides a possibility for real-time monitoring. However, sample shortages of SRMs highlights the need for monitoring accuracy, especially given the high cost associated with the design and development of SRM systems. Therefore, constructing datasets via regression simulations to compensate for SRM sample shortages is critical. To address this issue, we recommend adopting the level-set (LS) method to dynamically track the burning surface by solving partial differential equations (PDEs). The computational cost of numerical solution is prohibitive for scientific applications involving large-scale spatiotemporal domains. The physics-informed neural network (PINN) and neural operator have been used to accelerate the solution of PDE, showing satisfactory prediction performance and high computational efficiency. We designed a physics-guided network, named LS-PhyNet, that couples the potential physical mechanisms of burning surface regression into the deep learning framework. The proposed method is capable of encoding well-established traditional numerical discretization methods into the network architecture to leverage prior knowledge of underlying physics, thus providing the model with enhanced expressive power and interpretability. Experimental results prove that LS-PhyNet can better reproduce the burning surfaces obtained by numerical solution with only small data regimes, providing a new paradigm for real-time monitoring of burning surface regression transients during static ignition tests.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"357 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139839505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.1088/2632-2153/ad2493
A. Milder, A. S. Joglekar, W. Rozmus, D. H. Froula
Parameter estimation using observables is a fundamental concept in the experimental sciences. Mathematical models that represent the physical processes can enable reconstructions of the experimental observables and greatly assist in parameter estimation by turning it into an optimization problem which can be solved by gradient-free or gradient-based methods. In this work, the recent rise in flexible frameworks for developing differentiable scientific computing programs is leveraged in order to dramatically accelerate data analysis of a common experimental diagnostic relevant to laser–plasma and inertial fusion experiments, Thomson scattering. A differentiable Thomson-scattering data analysis tool is developed that uses reverse-mode automatic differentiation (AD) to calculate gradients. By switching from finite differencing to reverse-mode AD, three distinct outcomes are achieved. First, gradient descent is accelerated dramatically to the extent that it enables near real-time usage in laser–plasma experiments. Second, qualitatively novel quantities which require O ( 10 3 ) parameters can now be included in the analysis of data which enables unprecedented measurements of small-scale laser–plasma phenomena. Third, uncertainty estimation approaches that leverage the value of the Hessian become accurate and efficient because reverse-mode AD can be used for calculating the Hessian.
利用观测数据进行参数估计是实验科学的一个基本概念。表示物理过程的数学模型可以重构实验观测值，并通过将其转化为优化问题来极大地帮助参数估计，而优化问题可以通过无梯度或基于梯度的方法来解决。在这项工作中，我们利用了最近兴起的用于开发可微分科学计算程序的灵活框架，以显著加快与激光等离子体和惯性聚变实验相关的常见实验诊断--汤姆逊散射--的数据分析。我们开发了一种可微分的汤姆逊散射数据分析工具，它使用反向模式自动微分（AD）来计算梯度。通过从有限差分转换到反向模式自动差分，实现了三个不同的结果。首先，梯度下降的速度大大加快，在激光等离子体实验中几乎可以实时使用。其次，需要 O ( 10 3 ) 个参数的定性新量现在可以纳入数据分析，从而实现对小尺度激光等离子体现象的前所未有的测量。第三，利用赫塞斯值的不确定性估计方法变得精确而高效，因为反向模式 AD 可用于计算赫塞斯。
{"title":"Qualitative and quantitative enhancement of parameter estimation for model-based diagnostics using automatic differentiation with an application to inertial fusion","authors":"A. Milder, A. S. Joglekar, W. Rozmus, D. H. Froula","doi":"10.1088/2632-2153/ad2493","DOIUrl":"https://doi.org/10.1088/2632-2153/ad2493","url":null,"abstract":"\u0000 Parameter estimation using observables is a fundamental concept in the experimental sciences. Mathematical models that represent the physical processes can enable reconstructions of the experimental observables and greatly assist in parameter estimation by turning it into an optimization problem which can be solved by gradient-free or gradient-based methods. In this work, the recent rise in flexible frameworks for developing differentiable scientific computing programs is leveraged in order to dramatically accelerate data analysis of a common experimental diagnostic relevant to laser–plasma and inertial fusion experiments, Thomson scattering. A differentiable Thomson-scattering data analysis tool is developed that uses reverse-mode automatic differentiation (AD) to calculate gradients. By switching from finite differencing to reverse-mode AD, three distinct outcomes are achieved. First, gradient descent is accelerated dramatically to the extent that it enables near real-time usage in laser–plasma experiments. Second, qualitatively novel quantities which require \u0000 \u0000 \u0000 \u0000 O\u0000 \u0000 (\u0000 \u0000 10\u0000 3\u0000 \u0000 )\u0000 \u0000 \u0000 parameters can now be included in the analysis of data which enables unprecedented measurements of small-scale laser–plasma phenomena. Third, uncertainty estimation approaches that leverage the value of the Hessian become accurate and efficient because reverse-mode AD can be used for calculating the Hessian.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"133 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139780653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.1088/2632-2153/ad290c
M. Rezasefat, James D. Hogan
This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element simulation data. Two novel architectures, the Multi-Decoder CNN (MUDE-CNN) and the Multiple Encoder-Decoder Model with Transfer Learning (MTED-TL), were introduced to address the challenge of predicting the progressive and spatial evolutional of stress distributions around defects. The MUDE-CNN leveraged a shared encoder for simultaneous feature extraction and employed multiple decoders for distinct time frame predictions, while MTED-TL progressively transferred knowledge from one encoder-decoder block to another, thereby enhancing prediction accuracy through transfer learning. These models were evaluated to assess their accuracy, with a particular focus on predicting temporal stress fields around an additive manufacturing-induced isolated pore, as understanding such defects is crucial for assessing mechanical properties and structural integrity in materials and components fabricated via additive manufacturing. The temporal model evaluation demonstrated MTED-TL's consistent superiority over MUDE-CNN, owing to transfer learning's advantageous initialization of weights and smooth loss curves. Furthermore, an autoregressive training framework was introduced to improve temporal predictions, consistently outperforming both MUDE-CNN and MTED-TL. By accurately predicting temporal stress fields around AM-induced defects, these models can enable real-time monitoring and proactive defect mitigation during the fabrication process. This capability ensures enhanced component quality and enhances the overall reliability of additively manufactured parts.
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Pub Date : 2024-02-13DOI: 10.1088/2632-2153/ad290c
M. Rezasefat, James D. Hogan
This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element simulation data. Two novel architectures, the Multi-Decoder CNN (MUDE-CNN) and the Multiple Encoder-Decoder Model with Transfer Learning (MTED-TL), were introduced to address the challenge of predicting the progressive and spatial evolutional of stress distributions around defects. The MUDE-CNN leveraged a shared encoder for simultaneous feature extraction and employed multiple decoders for distinct time frame predictions, while MTED-TL progressively transferred knowledge from one encoder-decoder block to another, thereby enhancing prediction accuracy through transfer learning. These models were evaluated to assess their accuracy, with a particular focus on predicting temporal stress fields around an additive manufacturing-induced isolated pore, as understanding such defects is crucial for assessing mechanical properties and structural integrity in materials and components fabricated via additive manufacturing. The temporal model evaluation demonstrated MTED-TL's consistent superiority over MUDE-CNN, owing to transfer learning's advantageous initialization of weights and smooth loss curves. Furthermore, an autoregressive training framework was introduced to improve temporal predictions, consistently outperforming both MUDE-CNN and MTED-TL. By accurately predicting temporal stress fields around AM-induced defects, these models can enable real-time monitoring and proactive defect mitigation during the fabrication process. This capability ensures enhanced component quality and enhances the overall reliability of additively manufactured parts.
{"title":"Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks","authors":"M. Rezasefat, James D. Hogan","doi":"10.1088/2632-2153/ad290c","DOIUrl":"https://doi.org/10.1088/2632-2153/ad290c","url":null,"abstract":"\u0000 This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element simulation data. Two novel architectures, the Multi-Decoder CNN (MUDE-CNN) and the Multiple Encoder-Decoder Model with Transfer Learning (MTED-TL), were introduced to address the challenge of predicting the progressive and spatial evolutional of stress distributions around defects. The MUDE-CNN leveraged a shared encoder for simultaneous feature extraction and employed multiple decoders for distinct time frame predictions, while MTED-TL progressively transferred knowledge from one encoder-decoder block to another, thereby enhancing prediction accuracy through transfer learning. These models were evaluated to assess their accuracy, with a particular focus on predicting temporal stress fields around an additive manufacturing-induced isolated pore, as understanding such defects is crucial for assessing mechanical properties and structural integrity in materials and components fabricated via additive manufacturing. The temporal model evaluation demonstrated MTED-TL's consistent superiority over MUDE-CNN, owing to transfer learning's advantageous initialization of weights and smooth loss curves. Furthermore, an autoregressive training framework was introduced to improve temporal predictions, consistently outperforming both MUDE-CNN and MTED-TL. By accurately predicting temporal stress fields around AM-induced defects, these models can enable real-time monitoring and proactive defect mitigation during the fabrication process. This capability ensures enhanced component quality and enhances the overall reliability of additively manufactured parts.","PeriodicalId":503691,"journal":{"name":"Machine Learning: Science and Technology","volume":"48 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139841887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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