Physics-informed machine learning framework for creep-fatigue life prediction of a Ni-based superalloy using ensemble learning

Neural networks (NNs) are increasingly used in design to construct the objective functions and constraints, which leads to the needs of optimization of NN models with respect to design variables. A Neural Optimization Machine (NOM) is proposed for constrained single/multi-objective optimization by appropriately designing the NN architecture, activation function and loss function. The NN's built-in backpropagation algorithm conducts the optimization and is seamlessly integrated with the additive manufacturing (AM) process-property model. The NOM is tested using several numerical optimization problems. It is shown that the increase in the dimension of design variables does not increase the computational cost significantly. Next, a brief review of the physics-guided machine learning model for fatigue performance prediction of AM components is given. Finally, the NOM is applied to design processing parameters in AM to optimize the mechanical fatigue properties through the physics-guided NN under uncertainties. One novel contribution of the proposed methodology is that the constrained process optimization is integrated with physics/knowledge and the data-driven AM process-property model. Thus, a physics-compatible process design can be achieved. Another significant benefit is that the training and optimization are achieved in a unified NN model, and no separate process optimization is needed. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

Physics-based models for permeability prediction are essential for ensuring physically consistent reservoir characterization, yet they often fail to capture the full complexity of real geological systems. This study applies an explainable physics-informed machine learning (PIML) framework for discrepancy modeling to a legacy well dataset from a carbonate formation in southern Iraq. The dataset, originally comprising more than 2,400 samples from 10 wells, was heavily contaminated with incorrectly imputed or interpolated core permeability values. Through a rigorous zonation-based cleaning process, 1,130 high-quality samples were retained for modeling. A baseline physics model, built using well log–derived porosity and a porosity–permeability transform, was combined with a machine learning (ML) discrepancy model trained on three physics-informed features: neutron porosity (NPHI), the gamma ray/ bulk density (GR/RHOB) ratio, and depth. Three ML algorithms, Random Forest, XGBoost, and CatBoost, were evaluated, with five-fold cross-validation employed to ensure robustness. The discrepancy model effectively captured systematic biases in the physical model's predictions. When integrated into the boosted-ML frameworks, it delivered substantially higher permeability prediction performance, reducing root mean squared error (RMSE) by 56% and improving the adjusted coefficient of determination (adjusted R2) from −0.27 to 0.76 compared to the original physical model. These results demonstrate the strong potential of discrepancy-based ML-enhancement methodologies for improving permeability prediction in legacy well datasets. Explainable AI analysis using Shapley additive explanations (SHAP) to determine feature influence identified measured depth as the most influential predictor. Compaction effects not captured by the original physical model, GR/RHOB and NPHI also exerted substantial influence on the PIML permeability predictions. These findings not only improve permeability prediction performance but also reveal missing physics in the baseline model, enabling targeted refinement of physical relationships. This work highlights the feasibility and value of applying discrepancy-based PIML to noisy, incomplete legacy datasets, bridging the gap between empirical ML corrections and physically consistent modeling.

Landslide susceptibility mapping (LSM) is essential for regional geohazard assessment. However, conventional data-driven models often fail to capture slope-failure mechanisms and produce scientifically inconsistent predictions, particularly in regions with complex geology or limited data. This study proposes a novel physics-informed machine learning (PIML) framework that formulates LSM as a multi-objective optimisation problem. A positive-unlabelled (PU) bagging strategy identifies reliable non-landslide samples, and model training jointly minimises three complementary losses: a supervised loss ensuring predictive accuracy, a physical-consistency loss constraining monotonic relationships with the factor of safety (FoS), and a risk-consistency loss constraining monotonic relationships with the probability of failure (PoF). The FoS and PoF, derived from the simplified transient infiltration model (STIM) and the first-order reliability method (FORM), are incorporated into model training to enforce scientific consistency. A case study of rainfall-induced landslides in Gansu Province, China, shows that the proposed framework achieves Pareto-optimal trade-offs between accuracy and scientific consistency. Compared with the baseline, the optimal PIML model improved average AUC (0.882 vs. 0.870) under spatial cross-validation, reduced inconsistency by 73%, and outperformed the physically based probabilistic model. These results highlight that embedding geotechnical knowledge into ML produces susceptibility maps that are both accurate and scientifically meaningful.

Laser beam welding (LBW) of metallic components is a knowledge‑intensive manufacturing process whose quality depends on the complex multi‑physics. However, its engineering application is often hindered by the occurrence of porosity defects. Achieving a thorough understanding and reliable prediction of porosity defects remains difficult because it demands robust representation and reasoning over nonlinear and hard‑to‑observe physical information. In this study, we propose an integrated multimodal physics-informed machine learning (PIML) framework with the help of multi-physical modelling and experimental data to predict the porosity defects in laser beam welding of aluminum alloys. The whole framework contains a multimodal PIML model for predicting the porosity ratio and an ML-based estimator for relevant physical information. By utilizing the scalar welding parameters and high-dimensional physical information (probability of keyhole collapses, cumulative existing time of collapses, and molten pool geometry) as inputs, the multimodal PIML model shows great superiority in predicting the porosity ratio, with a reduction of the mean square error by 45%, compared with the ML model trained only with welding parameters. The ML-based estimator constructed with an encoder‐decoder architecture can accurately reproduce the critical physical information within a timeframe of seconds. By integrating these two ML models, the proposed framework advances engineering informatics by offering a scalable, physics-knowledge‑centric solution for fast and accurate porosity prediction in LBW manufacturing.

Physics-informed machine learning models for ship speed prediction

Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods

Heart failure (HF) is one of the major causes of morbidity and mortality globally, necessitating accurate tools for health outcome prediction and risk stratification. In this study, we propose an interpretable multimodal machine learning framework integrating four clinical data modalities (i.e., demographics, medications, laboratory tests, and electrocardiograms [ECGs]) to predict 30-day all-cause mortality and hospital readmission in HF patients. Using clinical data from 2868 HF patients across 43 local hospitals in Hong Kong, we trained and evaluated ten machine learning models for HF risk prediction, with the best performing model achieving an area under the receiver operating characteristic curve (AUC) of 0.881 for mortality and 0.709 for readmission. Notably, laboratory tests and ECG features dominate predictive power, and their combination alone yielded near-optimal results (AUC: 0.872), suggesting that these two modalities may be adequate for effective risk prediction in resource-constrained settings. The SHapley Additive exPlanations (SHAP) analysis identified serum albumin, high-sensitivity troponin I, lactate dehydrogenase, and QT interval dispersion as key predictors. Feature redundancy analysis further revealed strong correlations within laboratory tests and ECG features, suggesting opportunities for model simplification. To the best of our knowledge, this is the first study that comprehensively evaluates diverse configurations of four data modalities for HF risk prediction through ablation analysis, quantifying the marginal gains of each data modality and their combinations. Our findings demonstrate that an interpretable multimodal machine learning model can enhance risk prediction in HF patients, supporting personalized management and scalable deployment across diverse healthcare settings.

Predicting performance of in-situ microbial enhanced oil recovery process and screening of suitable microbe-nutrient combination from limited experimental data using physics informed machine learning approach

Student dropout in secondary schools is a major issue in developing countries, particularly in Sub-Saharan Africa. Sub-Saharan African countries had the highest dropout rate (37.5%), followed by South Asia (15.5%), the Middle East (11%), East Asia (9.5%), Latin America (7%), and Central Asia (3.5%). Various initiatives such as the big results now initiatives, no child left behind, and secondary education development programme as well as machine learning prediction models have been used to reduce the severity of the problem in Sub-Saharan countries. The ongoing dropout problem, particularly in secondary schools is ascribed to improper root cause identification and the absence of formal procedures that can be used to estimate the severity of the issue. This study has compared the AutoML model, ensemble learning approach, and KNORA-AutoML to predict student dropout problems. The KNORA-AutoML model scored 97% of accuracy, precision = 71%, and AUC = 87% when compared to the conventional ensemble of optimized ML models with accuracy = 96%, precision = 70%, and AUC = 78%. KNORA-AutoML model performance increased by 0.6% accuracy, 0.8% precision, and 8.7% AUC. An optimized model draws a lot of attention to the findings related to student dropout rates in developing countries.

Physics-informed machine learning models for predicting the progress of reactive-mixing

With the development of information and technology, especially with the boom in big data, healthcare support systems are becoming much better. Patient data can be collected, retrieved, and stored in real time. These data are valuable and meaningful for monitoring, diagnosing, and further applications in data analysis and decision-making. Essentially, the data can be divided into three types, namely, statistical, image-based, and sequential data. Each type has a different method of retrieval, processing, and deployment. Additionally, the application of machine learning (ML) and deep learning (DL) in healthcare support systems is growing more rapidly than ever. Numerous high-performance architectures are proposed to optimize decision-making. As reliability and stability are the most important factors in the healthcare support system, enhancing the predicted performance and maintaining the stability of the model are always the top priority. The main idea of our study comes from ensemble techniques. Numerous studies and data science competitions show that by combining several weak models into one, ensemble models can attain outstanding performance and reliability. We propose three deep ensemble learning (DEL) approaches, each with stable and reliable performance, that are workable on the above-mentioned data types. These are deep-stacked generalization ensemble learning, gradient deep learning boosting, and deep aggregation learning. The experiment results show that our proposed approaches achieve more vigorous and reliable performance than traditional ML and DL techniques on statistical, image-based, and sequential benchmark datasets. In particular, on the Heart Disease UCI dataset, representing the statistical type, the gradient deep learning boosting approach dominates the others with accuracy, recall, F1-score, Matthews correlation coefficient, and area under the curve values of 0.87, 0.81, 0.83, 0.73, and 0.91, respectively. On the X-ray dataset, representing the image-based type, the deep aggregation learning approach shows the highest performance with values of 0.91, 0.97, 0.93, 0.80, and 0.94, respectively. On the Depresjon dataset, representing the sequence type, the deep-stacked generalization ensemble learning approach outperforms the others with values of 0.91, 0.84, 0.86, 0.8, and 0.94, respectively. Overall, we conclude that applying DL models using our proposed approaches is a promising method for the healthcare support system to enhance prediction and diagnosis performance. Furthermore, our study reveals that these approaches are flexible and easy to apply to achieve optimal performance.

An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD) by ensuring robustness of the ML models’ interpretations. The dataset used comprises volumetric measurements from brain MRI and genetic data from healthy individuals and patients with MCI/AD, obtained through the Alzheimer’s Disease Neuroimaging Initiative. The existing class imbalance is addressed by an ensemble learning approach, while various attribution-based and counterfactual-based interpretability methods are leveraged towards producing diverse explanations related to the pathophysiology of MCI/AD. A unification method combining SHAP with counterfactual explanations assesses the interpretability techniques’ robustness. The best performing model yielded 87.5% balanced accuracy and 90.8% F1-score. The attribution-based interpretability methods highlighted significant volumetric and genetic features related to MCI/AD risk. The unification method provided useful insights regarding those features’ necessity and sufficiency, further showcasing their significance in MCI/AD diagnosis.

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

Rainfall plays a crucial role in agricultural practices, influencing the growth of essential crops and impacting economies reliant on agriculture. In Rajasthan, India, where agriculture is predominantly rainfed and dependent on unpredictable monsoon rains, accurate rainfall forecasting is vital for effective crop management, irrigation, optimizing agricultural productivity, managing water resources, and addressing drought conditions and. This study evaluates various machine learning models for predicting rainfall in East and West Rajasthan. The models assessed include Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Networks (ANN), and K-Nearest Neighbours (KNN). These models were compared based on their accuracy measures, including Root Mean Squared Error (RMSE), Relative Root Squared Error (RRSE), and Relative Absolute Error (RAE). The results indicate that ANN and KNN perform poorly compared to SVR and RF suggesting their inadequacy for rainfall forecasting in water-scarce regions. The performance of SVR and RF are comparable as the difference is less. However SVR is identified as the most effective model for rainfall prediction in both subdivisions, as highlighted in the study. This study suggest that SVR and RF model should be includes in prediction of amount of rainfall.

e13596 Background: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer (BC) and neoadjuvant therapy (NAT) has become an essential treatment strategy. Recent studies have showed tertiary lymphoid structures (TLSs) is associated with better treatment response to NAT in BC. However, there is still a lack of non-invasive biomarkers to predict the presence of TLSs in TNBC. This study aimed to develop a machine learning model for early identification and prediction of the presence of TLSs and treatment response to NAT in TNBC, which were essential for timely adjustments in treatment strategies for TNBC. Methods: In this study, 698 patients from multicenter were divided into a training cohort (n = 137), the TLSs validation cohort (n = 63) and the NAT response validation cohorts (n = 561). A total of 4788 radiomic features per patient were extracted from the intratumoral and peritumoral regions of DCE-MR. After extracting the optimal features, multiple machine learning models were developed to predict the presence of TLSs and were subsequently applied to predict the treatment response to NAT in NAT response validation cohorts. The performance of the models was assessed by area under curve values (AUC), and prognostic analysis were performed to evaluate its predictive value. Finally, the correlation between key radiomic features and key pathomic features was further analyzed to reveal the tumor heterogeneity of TNBC from a pathological perspective. Results: The XGBoost model, which outperformed all other predictive models, was selected as the radiomics-based TLS (rTLS) predictive model. It achieved AUCs of 0.922 in the training cohort, 0.852 in the TLS validation cohort, 0.724 in the Chinese-TNBC cohort, 0.919 in the DUKE cohort, and 0.883 in the I-SPY2 cohort. The rTLS predictive model demonstrated robust predictive performance, including across various patient subgroups defined by age, menopausal status, Ki67 level, cT stage, cN stage, and MammaPrint gene status. In YNCH cohort and DUKE cohort, prognostic analysis showed that low rTLS predictive score was significantly correlated with better disease-free survival in TNBC receiving NAT, and Cox regression analysis also confirmed the rTLS predictive score was a strong independent prognostic factor. Pathomic features further explained the pathological heterogeneity of TNBC with different responses to NAT. Conclusions: The rTLS predictive model, which accurately predicted the presence of TLSs and treatment response to NAT in TNBC, held promise for future clinical application in formulating personalized and effective treatment strategies for TNBC, ultimately improving prognosis.