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Saurabh Tiwari

yeungnam university

H指数:12

被引数:386

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Interpretable Machine Learning for Mechanical Property Prediction of 5Cr-0.5Mo Steel: SHAP Explainability, Multi-Model Comparison, and Uncertainty Quantification

可解释机器学习在5Cr-0.5Mo钢力学性能预测中的应用：SHAP可解释性、多模型比较和不确定性量化

IF2.5

2026-09-080

OA

Integrating Artificial Intelligence with Emerging Pharmaceutical Technologies: Current Progress, Clinical Translation, and Future Challenges

将人工智能与新兴制药技术相结合：当前进展、临床转化及未来挑战

International Journal of Molecular Sciences

IF4.9

2026-09-030

OA

Domain-Knowledge-Guided Feature Engineering for Small-Sample Machine Learning Prediction of Mechanical Properties in Low-Carbon Hot-Rolled Steel Strips

基于领域知识的特征工程用于低碳热轧钢带力学性能的小样本机器学习预测

IF2.5

2026-09-020

OA

Evaluating the Incremental Value of Three-Dimensional Conformer Descriptors for Blood–Brain Barrier Permeability Prediction Using the B3DB Benchmark Dataset

使用B3DB基准数据集评估三维构象描述符对血脑屏障渗透性预测的增量价值

IF5.5

2026-08-260

OA

Statistical Variability and Lower-Tail Performance Assessment of Tensile Properties in Flax, Jute, and Carbon Fiber Composite Laminates

亚麻、黄麻和碳纤维复合材料层压板的拉伸性能统计变异性及下尾性能评估

IF4.9

2026-08-120

OA

Quantitative Structural Thresholds for Blood–Brain Barrier Permeability Derived from Experimental logBB Measurements

基于实验logBB测量值的血脑屏障通透性的定量结构阈值

IF5.5

2026-08-110

OA

Correction: Gupta et al. A Correlation with the Deformation Stored Energy and Self-Annealing Behavior of ETP-Cu. Metals 2026, 16, 432

更正：Gupta等人. 变形储能与ETP-Cu自退火行为的关联. 金属 2026, 16, 432

IF2.5

2026-06-250

OA

Predicting Blood–Brain Barrier Permeability from Experimental Data: An Interpretable and Externally Validated Machine Learning Framework

基于实验数据预测血脑屏障通透性：一个可解释且外部验证的机器学习框架

IF5.5

2026-05-290

OAAI

Impact of higher alcohol injection rates and exhaust gas recirculation on efficiency, combustion, and NOx emissions in reactivity controlled compression ignition engines

提高醇类喷射速率和废气再循环对可控反应压缩点火发动机效率、燃烧和NOx排放的影响

Thermal Science and Engineering Progress

IF5.4

2026-05-230

PRE

Physics-Informed Neural Network Modelling of Hydrogen Diffusion and Trapping in Microalloyed Steels: A Data-Driven Synthesis Across Multiple Alloy Systems

基于物理信息的神经网络对微合金钢中氢扩散和陷阱捕获的建模：多合金体系的数据驱动合成

IF2.5

2026-05-190

OAAI

Machine Learning-Based Prediction of Stacking Fault Energy in High-Manganese Steels: A Comparative Study of Ensemble and Kernel Methods

基于机器学习的超高锰钢堆垛层错能预测：集成方法与核方法的比较研究

IF3.2

2026-05-090

OA

Physics-Informed Neural Networks for Process Optimization in Laser Powder Bed Fusion of Inconel 718 Superalloy: A Data-Efficient, Physics-Constrained Machine Learning Framework

用于Inconel 718高温合金激光粉末床融合过程优化的物理通知神经网络: 数据高效，物理约束的机器学习框架

IF2.5

2026-04-280

OA

A Correlation with the Deformation Stored Energy and Self-Annealing Behavior of ETP-Cu

与ETP-Cu的变形储存能和自退火行为的相关性

IF2.5

2026-04-170

OA

Machine Learning-Based Prediction of Multi-Year Cumulative Atmospheric Corrosion Loss in Low-Alloy Steels with SHAP Analysis

基于机器学习的低合金钢多年累积大气腐蚀损失预测及SHAP分析

IF2.8

2026-04-170

OAAI

Prediction of Hole Expansion Ratio in Advanced High-Strength Steels Using Physics-Informed Machine Learning

利用物理信息机器学习预测先进高强度钢的孔扩张比

IF3.2

2026-04-150

OAAI

Interpretable Machine-Learning Prediction of Atmospheric Zinc Corrosion Depth Under Diverse Environmental Conditions

可解释机器学习预测不同环境条件下的大气锌腐蚀深度

IF2.8

2026-04-100

OAAI

Anisotropy-driven grain morphology, orientation, and solute patterns during melt-pool solidification

各向异性驱动的晶粒形态、取向及溶质分布模式在熔池凝固过程中的表现

Progress in Additive Manufacturing

IF5.4

2026-04-010

PRE

A Machine Learning Framework for the Reconstruction of Composite Fatigue and Fracture Properties: A Synthetic Data Study

一种用于复合材料疲劳和断裂性能重建的机器学习框架：一项合成数据研究

IF3.2

2026-03-140

OAAI

Machine Learning Framework for Predicting Mechanical Properties of Heat-Treated Alloys: Computational Approach

机器学习框架预测热处理合金力学性能：计算方法

IF2.5

2026-03-130

OAAI

Machine Learning-Driven Prediction of Microstructural Evolution and Mechanical Properties in Heat-Treated Steels Using Gradient Boosting

机器学习驱动的热处理钢中微观结构演变和力学性能的梯度提升预测

IF2.4

2026-01-150

OA