Machine Learning Science and Technology最新文献_第9页

Incorporating background knowledge in symbolic regression using a computer algebra system 利用计算机代数系统在符号回归中纳入背景知识

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-02 DOI: 10.1088/2632-2153/ad4a1e

Charles Fox, Neil D Tran, F Nikki Nacion, Samiha Sharlin and Tyler R Josephson

{"title":"Incorporating background knowledge in symbolic regression using a computer algebra system","authors":"Charles Fox, Neil D Tran, F Nikki Nacion, Samiha Sharlin and Tyler R Josephson","doi":"10.1088/2632-2153/ad4a1e","DOIUrl":"https://doi.org/10.1088/2632-2153/ad4a1e","url":null,"abstract":"Symbolic regression (SR) can generate interpretable, concise expressions that fit a given dataset, allowing for more human understanding of the structure than black-box approaches. The addition of background knowledge (in the form of symbolic mathematical constraints) allows for the generation of expressions that are meaningful with respect to theory while also being consistent with data. We specifically examine the addition of constraints to traditional genetic algorithm (GA) based SR (PySR) as well as a Markov-chain Monte Carlo (MCMC) based Bayesian SR architecture (Bayesian Machine Scientist), and apply these to rediscovering adsorption equations from experimental, historical datasets. We find that, while hard constraints prevent GA and MCMC SR from searching, soft constraints can lead to improved performance both in terms of search effectiveness and model meaningfulness, with computational costs increasing by about an order of magnitude. If the constraints do not correlate well with the dataset or expected models, they can hinder the search of expressions. We find incorporating these constraints in Bayesian SR (as the Bayesian prior) is better than by modifying the fitness function in the GA.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141258806","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}

引用次数: 0

GPU optimization techniques to accelerate optiGAN-a particle simulation GAN. 利用 GPU 优化技术加速 OptiGAN--粒子模拟 GAN。

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-06-01 Epub Date: 2024-06-13 DOI: 10.1088/2632-2153/ad51c9

Anirudh Srikanth, Carlotta Trigila, Emilie Roncali

{"title":"GPU optimization techniques to accelerate optiGAN-a particle simulation GAN.","authors":"Anirudh Srikanth, Carlotta Trigila, Emilie Roncali","doi":"10.1088/2632-2153/ad51c9","DOIUrl":"10.1088/2632-2153/ad51c9","url":null,"abstract":"<p><p>The demand for specialized hardware to train AI models has increased in tandem with the increase in the model complexity over the recent years. Graphics processing unit (GPU) is one such hardware that is capable of parallelizing operations performed on a large chunk of data. Companies like Nvidia, AMD, and Google have been constantly scaling-up the hardware performance as fast as they can. Nevertheless, there is still a gap between the required processing power and processing capacity of the hardware. To increase the hardware utilization, the software has to be optimized too. In this paper, we present some general GPU optimization techniques we used to efficiently train the optiGAN model, a Generative Adversarial Network that is capable of generating multidimensional probability distributions of optical photons at the photodetector face in radiation detectors, on an 8GB Nvidia Quadro RTX 4000 GPU. We analyze and compare the performances of all the optimizations based on the execution time and the memory consumed using the Nvidia Nsight Systems profiler tool. The optimizations gave approximately a 4.5x increase in the runtime performance when compared to a naive training on the GPU, without compromising the model performance. Finally we discuss optiGANs future work and how we are planning to scale the model on GPUs.</p>","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"5 2","pages":"027001"},"PeriodicalIF":6.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11170465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141331906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformer-powered surrogates close the ICF simulation-experiment gap with extremely limited data 变压器供电的代用设备利用极其有限的数据缩小了 ICF 模拟与实验之间的差距

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-05-30 DOI: 10.1088/2632-2153/ad4e03

Matthew L Olson, Shusen Liu, Jayaraman J Thiagarajan, Bogdan Kustowski, Weng-Keen Wong and Rushil Anirudh

引用次数: 0

Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems 发现复杂动力系统数据中流形维度和坐标的自动编码器

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-05-29 DOI: 10.1088/2632-2153/ad4ba5

Kevin Zeng, Carlos E Pérez De Jesús, Andrew J Fox and Michael D Graham

{"title":"Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems","authors":"Kevin Zeng, Carlos E Pérez De Jesús, Andrew J Fox and Michael D Graham","doi":"10.1088/2632-2153/ad4ba5","DOIUrl":"https://doi.org/10.1088/2632-2153/ad4ba5","url":null,"abstract":"While many phenomena in physics and engineering are formally high-dimensional, their long-time dynamics often live on a lower-dimensional manifold. The present work introduces an autoencoder framework that combines implicit regularization with internal linear layers and L2 regularization (weight decay) to automatically estimate the underlying dimensionality of a data set, produce an orthogonal manifold coordinate system, and provide the mapping functions between the ambient space and manifold space, allowing for out-of-sample projections. We validate our framework’s ability to estimate the manifold dimension for a series of datasets from dynamical systems of varying complexities and compare to other state-of-the-art estimators. We analyze the training dynamics of the network to glean insight into the mechanism of low-rank learning and find that collectively each of the implicit regularizing layers compound the low-rank representation and even self-correct during training. Analysis of gradient descent dynamics for this architecture in the linear case reveals the role of the internal linear layers in leading to faster decay of a ‘collective weight variable’ incorporating all layers, and the role of weight decay in breaking degeneracies and thus driving convergence along directions in which no decay would occur in its absence. We show that this framework can be naturally extended for applications of state-space modeling and forecasting by generating a data-driven dynamic model of a spatiotemporally chaotic partial differential equation using only the manifold coordinates. Finally, we demonstrate that our framework is robust to hyperparameter choices.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"9 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195813","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}

引用次数: 0

Unsupervised learning of quantum many-body scars using intrinsic dimension 利用本征维度对量子多体伤痕进行无监督学习

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-05-28 DOI: 10.1088/2632-2153/ad4d3f

Harvey Cao, Dimitris G Angelakis and Daniel Leykam

引用次数: 0

Learning the dynamics of a one-dimensional plasma model with graph neural networks 利用图神经网络学习一维等离子体模型的动力学特性

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-05-27 DOI: 10.1088/2632-2153/ad4ba6

Diogo D Carvalho, Diogo R Ferreira and Luís O Silva

引用次数: 0

Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes 用于模拟复杂形状计算流体动力学的混合量子物理信息神经网络

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-05-21 DOI: 10.1088/2632-2153/ad43b2

Alexandr Sedykh, Maninadh Podapaka, Asel Sagingalieva, Karan Pinto, Markus Pflitsch and Alexey Melnikov

{"title":"Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes","authors":"Alexandr Sedykh, Maninadh Podapaka, Asel Sagingalieva, Karan Pinto, Markus Pflitsch and Alexey Melnikov","doi":"10.1088/2632-2153/ad43b2","DOIUrl":"https://doi.org/10.1088/2632-2153/ad43b2","url":null,"abstract":"Finding the distribution of the velocities and pressures of a fluid by solving the Navier–Stokes equations is a principal task in the chemical, energy, and pharmaceutical industries, as well as in mechanical engineering and in design of pipeline systems. With existing solvers, such as OpenFOAM and Ansys, simulations of fluid dynamics in intricate geometries are computationally expensive and require re-simulation whenever the geometric parameters or the initial and boundary conditions are altered. Physics-informed neural networks (PINNs) are a promising tool for simulating fluid flows in complex geometries, as they can adapt to changes in the geometry and mesh definitions, allowing for generalization across fluid parameters and transfer learning across different shapes. We present a hybrid quantum PINN (HQPINN) that simulates laminar fluid flow in 3D Y-shaped mixers. Our approach combines the expressive power of a quantum model with the flexibility of a PINN, resulting in a 21% higher accuracy compared to a purely classical neural network. Our findings highlight the potential of machine learning approaches, and in particular HQPINN, for complex shape optimization tasks in computational fluid dynamics. By improving the accuracy of fluid simulations in complex geometries, our research using hybrid quantum models contributes to the development of more efficient and reliable fluid dynamics solvers.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"56 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146223","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}

引用次数: 0

Unifying O(3) equivariant neural networks design with tensor-network formalism 用张量网络形式主义统一 O(3) 等变神经网络设计

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-05-20 DOI: 10.1088/2632-2153/ad4a04

Zimu Li, Zihan Pengmei, Han Zheng, Erik Thiede, Junyu Liu and Risi Kondor

{"title":"Unifying O(3) equivariant neural networks design with tensor-network formalism","authors":"Zimu Li, Zihan Pengmei, Han Zheng, Erik Thiede, Junyu Liu and Risi Kondor","doi":"10.1088/2632-2153/ad4a04","DOIUrl":"https://doi.org/10.1088/2632-2153/ad4a04","url":null,"abstract":"Many learning tasks, including learning potential energy surfaces from ab initio calculations, involve global spatial symmetries and permutational symmetry between atoms or general particles. Equivariant graph neural networks are a standard approach to such problems, with one of the most successful methods employing tensor products between various tensors that transform under the spatial group. However, as the number of different tensors and the complexity of relationships between them increase, maintaining parsimony and equivariance becomes increasingly challenging. In this paper, we propose using fusion diagrams, a technique widely employed in simulating SU(2)-symmetric quantum many-body problems, to design new spatial equivariant components for neural networks. This results in a diagrammatic approach to constructing novel neural network architectures. When applied to particles within a given local neighborhood, the resulting components, which we term ‘fusion blocks,’ serve as universal approximators of any continuous equivariant function defined on the neighborhood. We incorporate a fusion block into pre-existing equivariant architectures (Cormorant and MACE), leading to improved performance with fewer parameters on a range of challenging chemical problems. Furthermore, we apply group-equivariant neural networks to study non-adiabatic molecular dynamics of stilbene cis-trans isomerization. Our approach, which combines tensor networks with equivariant neural networks, suggests a potentially fruitful direction for designing more expressive equivariant neural networks.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"46 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146255","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}

引用次数: 0

Feature selection for high-dimensional neural network potentials with the adaptive group lasso 利用自适应群套索为高维神经网络电位进行特征选择

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-05-16 DOI: 10.1088/2632-2153/ad450e

Johannes Sandberg, Thomas Voigtmann, Emilie Devijver and Noel Jakse

{"title":"Feature selection for high-dimensional neural network potentials with the adaptive group lasso","authors":"Johannes Sandberg, Thomas Voigtmann, Emilie Devijver and Noel Jakse","doi":"10.1088/2632-2153/ad450e","DOIUrl":"https://doi.org/10.1088/2632-2153/ad450e","url":null,"abstract":"Neural network potentials are a powerful tool for atomistic simulations, allowing to accurately reproduce ab initio potential energy surfaces with computational performance approaching classical force fields. A central component of such potentials is the transformation of atomic positions into a set of atomic features in a most efficient and informative way. In this work, a feature selection method is introduced for high dimensional neural network potentials, based on the adaptive group lasso (AGL) approach. It is shown that the use of an embedded method, taking into account the interplay between features and their action in the estimator, is necessary to optimize the number of features. The method’s efficiency is tested on three different monoatomic systems, including Lennard–Jones as a simple test case, Aluminium as a system characterized by predominantly radial interactions, and Boron as representative of a system with strongly directional components in the interactions. The AGL is compared with unsupervised filter methods and found to perform consistently better in reducing the number of features needed to reproduce the reference simulation data at a similar level of accuracy as the starting feature set. In particular, our results show the importance of taking into account model predictions in feature selection for interatomic potentials.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"51 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141060611","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}

引用次数: 0

A multifidelity approach to continual learning for physical systems 物理系统持续学习的多保真方法

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-05-15 DOI: 10.1088/2632-2153/ad45b2

Amanda Howard, Yucheng Fu and Panos Stinis

引用次数: 0