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Machine learning for predicting the outcome of terminal ballistics events

Defence Technology（防务技术） 2024年第1期31卷 14-26页

作者： Shannon Ryan Neeraj Mohan Sushma Arun Kumar AV Julian Berk Tahrima Hashem Santu Rana svetha venkatesh Applied Artificial Intelligence Institute(A2I2) Deakin University75 Pigdons RdWaurn PondsVIC3216Australia

Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal *** evaluate the performance of four popular ML-based regression models,extreme gradient boosting(XGBoost),artificial neural network(ANN),support vector regression(SVR),and Gaussian process regression(GP),on two common terminal ballistics’ problems:(a)predicting the V50ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments,and(b) predicting the depth to which a projectile will penetrate a target of semi-infinite *** achieve this we utilise two datasets,each consisting of approximately 1000samples,collated from public release *** demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this *** extrapolation is not advisable for ML-based regression models,for applications such as lethality/survivability analysis,such capability is *** circumvent this,we implement expert knowledge and physics-based models via enforced monotonicity,as a Gaussian prior mean,and through a modified loss *** physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models,providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when *** resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types,target materials and thicknesses,and impact conditions significantly more diverse than that achievable from any existing analytical *** with numerical analysis tools such as finite element solvers the ML models run orders of magnitude *** provide some general guidelines throughout for the development,application,and reporting of ML models in terminal ballist

关键词： Machine learning Artificial intelligence Physics-informed machine learning Terminal ballistics Armour

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A bayesian optimisation methodology for the inverse derivation of viscoplasticity model constants in high strain-rate simulations

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Defence Technology（防务技术） 2022年第9期18卷 1563-1577页

作者： Shannon Ryan Julian Berk Santu Rana Brodie McDonald svetha venkatesh Applied Artificial Intelligence Institute(A^(2)I^(2)) Deakin University75 Pigdons RdWaurn PondsVIC3216Australia Defence Science and Technology Group 506 Lorimer StreetFishermans BendVIC3207Australia

We present an inverse methodology for deriving viscoplasticity constitutive model parameters for use in explicit finite element simulations of dynamic processes using functional experiments, i.e., those which provide value beyond that of constitutive model development. The developed methodology utilises Bayesian optimisation to minimise the error between experimental measurements and numerical simulations performed in LS-DYNA. We demonstrate the optimisation methodology using high hardness armour steels across three types of experiments that induce a wide range of loading conditions: ballistic penetration, rod-on-anvil, and near-field blast deformation. By utilising such a broad range of conditions for the optimisation, the resulting constitutive model parameters are generalised, i.e., applicable across the range of loading conditions encompassed the by those experiments(e.g., stress states, plastic strain magnitudes, strain rates, etc.). Model constants identified using this methodology are demonstrated to provide a generalisable model with superior predictive accuracy than those derived from conventional mechanical characterisation experiments or optimised from a single experimental condition.

关键词： Constitutive modelling Finite element Bayesian optimisation Finite element model updating

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Machine learning for predicting the outcome of terminal ballistics events

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Defence Technology 2024年第1期

作者： Shannon Ryan Neeraj Mohan Sushma Arun Kumar AV Julian Berk Tahrima Hashem Santu Rana svetha venkatesh

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Machine learning-based discovery of vibrationally stable materials

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npj Computational Materials 2023年第1期9卷 2304-2309页

作者： Sherif Abdulkader Tawfik Mahad Rashid Sunil Gupta Salvy P.Russo Tiffany R.Walsh svetha venkatesh Institute for Frontier Materials Deakin UniversityGeelongVIC 3216Australia ARC Centre of Excellence in Exciton Science RMIT UniversityMelbourneVIC 3001Australia Applied Artificial Intelligence Institute(A2I2) Deakin UniversityGeelongVIC 3216Australia Chemical and Quantum Physics School of ScienceRMIT UniversityMelbourneVIC 3001Australia

The identification of the ground state phases of a chemical space in the convex hull analysis is a key determinant of the synthesizability of *** material databases have been instrumental in exploring one aspect of the synthesizability of many materials,namely thermodynamic ***,the vibrational stability,which is another aspect of synthesizability,of new materials is not *** first principles approaches to calculate the vibrational spectra of materials in online material databases is computationally ***,a dataset of vibrational stability for~3100 materials is used to train a machine learning classifier that can accurately distinguish between vibrationally stable and unstable *** classifier has the potential to be further developed as an essential filtering tool for online material databases that can inform the material science community of the vibrational stability or instability of the materials queried in convex hulls.

关键词： materials stability vibrational

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监控系统中的多摄像机协同(英文)

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自动化学报 2003年第3期29卷 408-422页

作者： Nam T. Nguyen svetha venkatesh Geoff West Hung H. Bui Department of Computing Curtin University of TechnologyDepartment of Computing Curtin University of TechnologyDepartment of Computing Curtin University of TechnologyDepartment of Computing Curtin University of Technology Perth AustraliaPerth AustraliaPerth AustraliaPerth Australia

描述了一个用于室内场合对多个目标进行跟踪的分布式监控系统 .该系统由多个廉价的固定镜头的摄像机构成 ,具有多个摄像机处理模块和一个中央模块用于协调摄像机间的跟踪任务 .由于每个运动目标有可能被多个摄像机同时跟踪 ,因此如何选... 详细信息

描述了一个用于室内场合对多个目标进行跟踪的分布式监控系统 .该系统由多个廉价的固定镜头的摄像机构成 ,具有多个摄像机处理模块和一个中央模块用于协调摄像机间的跟踪任务 .由于每个运动目标有可能被多个摄像机同时跟踪 ,因此如何选择最合适的摄像机对某一目标跟踪 ,特别是在系统资源紧张时 ,成为一个问题 .提出的新算法能根据目标与摄像机之间的距离并考虑到遮挡的情况 ,把目标分配给相应的摄像机 ,因此在遮挡出现时 ,系统能把遮挡的目标分配给能看见目标并距离最近的那个摄像机 .实验表明该系统能协调好多个摄像机进行目标跟踪。

关键词：分布式监控系统目标跟踪摄像机处理模块摄像机协同

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A bayesian optimisation methodology for the inverse derivation of viscoplasticity model constants in high strain-rate simulations

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Defence Technology 2022年第9期

作者： Shannon Ryan Julian Berk Santu Rana Brodie Mc Donald svetha venkatesh

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我们只相信我们想去相信的

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中学生英语 2024年第7期 21-25页

作者： svetha Mahadevan 任新超(译) 不详湖北省襄阳市东津新区金源中学

银灰色的天空无情地轰隆着。乌云遮住了太阳。空旷的声音被隆隆的雷声打断了。随着雨越来越大,水坑开始发出巨大的声音。汽车的车顶被溅起的水撞得砰砰作响。冰冷的雨落在吉塔的头上,但她还是跑过了湿滑的小路。瓢泼大雨摧毁了天空清澈... 详细信息

银灰色的天空无情地轰隆着。乌云遮住了太阳。空旷的声音被隆隆的雷声打断了。随着雨越来越大,水坑开始发出巨大的声音。汽车的车顶被溅起的水撞得砰砰作响。冰冷的雨落在吉塔的头上,但她还是跑过了湿滑的小路。瓢泼大雨摧毁了天空清澈的倒影,搅得一片混沌。

关键词：混沌雷声倒影

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