Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys(HEAs),especially when integrating computational predictions with sparse experimental observ...Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys(HEAs),especially when integrating computational predictions with sparse experimental observations.This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes(cGP),Deep Gaussian processes(DGP),encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting(XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V.We specifically assess their capabilities in predicting correlated material properties,including yield strength,hardness,modulus,ultimate tensile strength,elongation,and average hardness under dynamic/quasi-static conditions,alongside auxiliary computational properties.The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic,heterotopic,and incomplete data commonly encountered in materials science.Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperformother surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge.This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and dataefficient materials design.展开更多
为了准确预测与深层页岩气藏压裂改造相关的两项重要指标——杨氏模量和泊松比,基于三轴抗压强度实验结果,采用高斯过程回归(Gaussian Process Regression,GPR)方法,建立了四川盆地东南部林滩场地区奥陶系上统五峰组—志留系下统龙马溪...为了准确预测与深层页岩气藏压裂改造相关的两项重要指标——杨氏模量和泊松比,基于三轴抗压强度实验结果,采用高斯过程回归(Gaussian Process Regression,GPR)方法,建立了四川盆地东南部林滩场地区奥陶系上统五峰组—志留系下统龙马溪组一段(以下简称龙一段)深层页岩气储层的岩石力学参数预测模型,并对计算得到的杨氏模量和泊松比进行了定量评价。研究结果表明:①该区深层页岩储层样品受内部应力薄弱面的影响,随温度和压力的升高,应力—应变曲线在峰后阶段的波动特征更为明显;②GPR模型可以降低页岩储层“纵向异性、横观同性”的影响,残差分布均表现为近似对称的等腰三角形特征,训练时间较短、预测速度较快,岩石力学参数(杨氏模量和泊松比)的预测准确率和GPR模型的置信度均超过90%,预测精度得以显著提高;③单井岩石力学参数(杨氏模量和泊松比)预测曲线与岩石力学实验结果具有较好的拟合效果,可以真实地反映该区五峰组—龙一段深层页岩储层的岩石力学性质。结论认为,五峰组—龙一段储层的③号层底部和②号层具有较强的脆性特征和良好的工程改造条件,是该区深层页岩气后续开发的主力层段。展开更多
为响应碳达峰、碳中和的需求,构建一套完整的"源-网-荷-储"的新能源电力系统,提出了一种基于Hamiltonian Monte Carlo推断深度高斯过程(HMCDGP)算法的分布式光伏净负荷预测模型.首先,分别使用直接预测和间接预测两种形式对预...为响应碳达峰、碳中和的需求,构建一套完整的"源-网-荷-储"的新能源电力系统,提出了一种基于Hamiltonian Monte Carlo推断深度高斯过程(HMCDGP)算法的分布式光伏净负荷预测模型.首先,分别使用直接预测和间接预测两种形式对预测模型的精度进行实验并得到点预测结果;其次,使用所提出的模型进行概率预测实验并得到区间预测结果;最后,通过以澳洲电网记录的300户净负荷数据为基础的对比实验验证所提模型的优越性.在得到准确的净负荷概率预测后,可以通过电力调度充分利用光伏产出,减少化石能源使用,从而减少碳排放.展开更多
基金supported by the Texas A&M University System National Laboratories Office of the Texas A&M University System and Los Alamos National Laboratory as part of the Joint Research Collaboration Program. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Los Alamos National Laboratory or The Texas A&M University Systemsupport from the U.S. Department of Energy (DOE) ARPA-E CHADWICK Program through Project DE‐AR0001988JJ acknowledges support from the Los Alamos National Laboratory Laboratory (LANL) Laboratory Directed Research and Development Program under project number 20220815PRD4. LANL is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy (Contract No. 89233218CNA000001). Original data were generated within the BIRDSHOT Center (https://birdshot.tamu.edu), supported by the Army Research Laboratory under Cooperative Agreement (CA) NumberW911NF-22-2-0106 (MM, DK, DA, VA and RA acknowledge partial support from this CA). NF acknowledges support from AFRL through a subcontract with ARCTOS, TOPS VI (165852-19F5830-19-02-C1). Calculations were carried out at Texas A&M High-Performance Research Computing (HPRC).
文摘Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys(HEAs),especially when integrating computational predictions with sparse experimental observations.This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes(cGP),Deep Gaussian processes(DGP),encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting(XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V.We specifically assess their capabilities in predicting correlated material properties,including yield strength,hardness,modulus,ultimate tensile strength,elongation,and average hardness under dynamic/quasi-static conditions,alongside auxiliary computational properties.The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic,heterotopic,and incomplete data commonly encountered in materials science.Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperformother surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge.This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and dataefficient materials design.
文摘为了准确预测与深层页岩气藏压裂改造相关的两项重要指标——杨氏模量和泊松比,基于三轴抗压强度实验结果,采用高斯过程回归(Gaussian Process Regression,GPR)方法,建立了四川盆地东南部林滩场地区奥陶系上统五峰组—志留系下统龙马溪组一段(以下简称龙一段)深层页岩气储层的岩石力学参数预测模型,并对计算得到的杨氏模量和泊松比进行了定量评价。研究结果表明:①该区深层页岩储层样品受内部应力薄弱面的影响,随温度和压力的升高,应力—应变曲线在峰后阶段的波动特征更为明显;②GPR模型可以降低页岩储层“纵向异性、横观同性”的影响,残差分布均表现为近似对称的等腰三角形特征,训练时间较短、预测速度较快,岩石力学参数(杨氏模量和泊松比)的预测准确率和GPR模型的置信度均超过90%,预测精度得以显著提高;③单井岩石力学参数(杨氏模量和泊松比)预测曲线与岩石力学实验结果具有较好的拟合效果,可以真实地反映该区五峰组—龙一段深层页岩储层的岩石力学性质。结论认为,五峰组—龙一段储层的③号层底部和②号层具有较强的脆性特征和良好的工程改造条件,是该区深层页岩气后续开发的主力层段。
文摘为响应碳达峰、碳中和的需求,构建一套完整的"源-网-荷-储"的新能源电力系统,提出了一种基于Hamiltonian Monte Carlo推断深度高斯过程(HMCDGP)算法的分布式光伏净负荷预测模型.首先,分别使用直接预测和间接预测两种形式对预测模型的精度进行实验并得到点预测结果;其次,使用所提出的模型进行概率预测实验并得到区间预测结果;最后,通过以澳洲电网记录的300户净负荷数据为基础的对比实验验证所提模型的优越性.在得到准确的净负荷概率预测后,可以通过电力调度充分利用光伏产出,减少化石能源使用,从而减少碳排放.
