Neural surrogate-driven modelling, optimisation, and generation of engineering designs: A concise review

Aim/Purpose: The study investigates the factors influencing the acceptance and utilisation of large language models (LLMs) (predictor variables of LLM usage), such as ChatGPT, in Learning design by instructional designers and university-teaching academics from various countries. Background: Large language models (LLMs) have exploded onto the scene, transforming the landscape of learning design. Instructional designers and university teaching academics have been overburdened with content creation for their teaching programmes, and the arrival of LLM models will help in this regard by developing more interactive content that drives student engagement and, in turn, contributes to student success. Since LLMs are a relatively new phenomenon, little is known about the factors influencing their acceptance in learning design; therefore, this research is needed, as learning design principles are the bedrock of student engagement and success. Methodology: A cross-sectional correlational quantitative study was employed. Data was collected using an online questionnaire posted on social media, including LinkedIn, from 203 instructional designers and university teaching academics. Purposive and snowball sampling methods were used to target instructional designers and university teaching academics at colleges and universities worldwide. Participants were asked to share the survey link with fellow instructional designers and university-teaching academics in their communities. The factor structure of the data was determined using exploratory factor analysis. Nonetheless, the factor structure derived from the LLMs did not entirely reflect the original configuration of the Unified Theory of Acceptance and Use of Technology (UTAUT3), as certain predictors appeared to coalesce, indicating LLMs’ unique nature in learning design. Confirmatory factor analysis was used to verify the fit of the data on the measurement model. First-order and second-order structural modelling were used to identify the structural relationships among the variables. Contribution: The study determines significant factors for the acceptance of LLMs by instructional designers and academic teaching staff in learning design, enabling possible opportunities for best practices in the field through interventions to optimize LLM usage. The study applies the technology acceptance model to the emerging LLM technology and extends the technology acceptance model by adding the trust construct as a predictor variable. Findings: The structural analysis results indicated that the ingrained LLM practices, LLM peer-driven expectations, innovative propensity towards LLM adoption, reliability and provider trust in LLMs, and ease of use and support influenced perceived LLM benefits and usage, but community standards and infrastructure had no influence. The second-order structural equation modelling indicated that perceived LLM benefits and usage and ingrained LLM habits contributed most to the learning design. Recommendations for Practitioners: Teaching academics and instructional designers must use LLMs in designing content, assessments, and interactive learning activities, and attend LLM training workshops on prompting and best practices in integrating LLMs into learning and teaching to see their benefits; hence, regular use of LLMs will then lead to trust and innovation in LLMs usage, enhancing learning design and improving student learning outcomes. Recommendation for Researchers: Researchers must use mixed methods approaches to have a deeper understanding of the factors influencing LLMs. Since habit and perceived LLM benefits and usage contributed the most variance to learning design, researchers must investigate strategies that optimise these factors in learning design, such as effective intervention strategies that can help form positive LLM habits. In addition, the findings provide researchers with a starting point for future research. Further researchers must investigate interventions that optimise the influence of personal innovativeness and trust that contributed the least variance to learning design, hence unlocking the potential of LLMs in learning design through innovation, responsible, and ethical use. Impact on Society: The use of LLMs in learning design has a high possibility of transforming education, specifically the learning design landscape. Using LLMs will free up more time for teaching academics and instructional designers so that they spend more time on higher-order thinking skill demands. Consequently, the students will be exposed to more engaging and interactive content, resulting in improved learning outcomes. Future Research: Future research must include context-derived external variables in technology acceptance models, such as levels of prompting competencies, to provide a deeper understanding of LLMs. In addition, future research must be based on the application and impact of LLMs on student engagement and success, and their attainment of 21st-century skills.

Multi-fidelity neural network for uncertainty quantification of chemical reaction models

Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of LLMs in downstream NLP applications effectively. This can be accomplished through a variety of approaches, including augmented prompting, instruction prompt tuning, and reinforcement learning from human feedback (RLHF). Our focus is on making LLMs accessible to the broader biomedical informatics community, including clinicians and researchers who may be unfamiliar with NLP. Additionally, NLP practitioners may gain insight from the described best practices. We focus on 3 broad categories of NLP tasks, namely natural language understanding, natural language inferencing, and natural language generation. We review the emerging trends in prompt tuning, instruction fine-tuning, and evaluation metrics used for LLMs while drawing attention to several issues that impact biomedical NLP applications, including falsehoods in generated text (confabulation/hallucinations), toxicity, and dataset contamination leading to overfitting. We also review potential approaches to address some of these current challenges in LLMs, such as chain of thought prompting, and the phenomena of emergent capabilities observed in LLMs that can be leveraged to address complex NLP challenge in biomedical applications.

In this study, high-Fidelity Large eddy simulation (LES) data was used to create a surrogate turbulence model to enhance the estimation of turbulent heat flux in a Reynolds averaged Navier Stokes (RANS) solver for film cooling applications. The LES simulation (A flat plate film cooling application with a Reynolds number of 17,382, a blowing ratio of 1 and a density ratio of 2) was validated by comparison with experimental data. A correlation analysis was performed to identify the most influential parameters, and temperature gradient, velocity gradient, turbulent dissipation rate, shear strain rate, and turbulent viscosity ratio were selected as the most effective parameters. A second model was developed to create a Galilean-invariant model that remains unaffected by changes in the coordinate system. Neural network was applied to the LES training data to propose a modified surrogate dynamic turbulent Prandtl number to be applied to the k − ε realizable RANS simulation. The first surrogate model was implemented to two different geometries, one geometry was a flat plate film cooling similar to the training data but with different flow conditions, and another one was a more complex geometry with pressure gradient. The second model was only implemented on the second geometry and produced similar results. The surrogate models predicted a more accurate thermal field near the cooling wall (up-to 58 % error reduction) with a computational time complexity similar to the base RANS solver.

This paper proposes the use of Artificial Neural Networks (ANNs), specifically Physics-Informed Neural Networks (PINNs), for dynamic surrogate modelling of wind turbines. PINNs offer the flexibility to model complex relationships while incorporating physics-based constraints, enabling accurate representation of wind turbine dynamics. In this paper, a PINN-based surrogate model is developed for the Blade Element Momentum (BEM) aerodynamic model used in state-of-the-art numerical wind turbine simulations. The PINN model replaces the time-consuming root-finding process in BEM with high-dimensional regression, significantly improving computational efficiency. The PINN model is trained using data generated from a numerical model of the IEA-15MW reference wind turbine, and its performance is compared against conventional data-driven Neural Network (DDNN) models. The proposed surrogate model provides more efficient and accurate evaluations of wind turbine responses compared with traditional surrogate modelling approaches. A significant computational advantage is obtained by using the developed surrogate models with a forty-fold speedup demonstrated compared to the BEM model. Replacing the BEM model with the PINN-based surrogate model for load computation in the numerical model used for dynamic analysis results in an overall reduction of 35% in computational time for a complete dynamic simulation. This is a substantial improvement in efficiency without sacrificing accuracy — the maximum Mean Absolute Error (MAE) values for the surrogate models are of the order of 10−2, which shows that the surrogate models can predict the angle of attack at any blade node with a discrepancy of less than 0.5°. The surrogate models significantly reduce computational time while maintaining high accuracy, making them a promising approach for simulating wind turbine dynamics, especially in fields such as reliability analysis or fatigue estimation where many simulations are necessary.

<sec> <title>BACKGROUND</title> Early prediction of gestational diabetes mellitus (GDM) is critical for improving maternal health outcomes. However, predictive models are often challenged by limited early-pregnancy samples, severe class imbalance in datasets, and complex interrelationships among clinical features. </sec> <sec> <title>OBJECTIVE</title> This study aimed to develop and evaluate a unified dual-dimensional enhancement framework that integrates data augmentation and feature engineering to improve early GDM prediction performance by addressing data imbalance and leveraging medical prior knowledge. </sec> <sec> <title>METHODS</title> We proposed a framework combining Generative Adversarial Network (GAN)-based data augmentation with Large Language Model (LLM)-inspired feature engineering. GAN sampling was used to generate clinically plausible synthetic minority class samples to mitigate data imbalance. The LLM was guided to organize features into domains (e.g., basic demographics, metabolic syndrome, core liver biomarkers) and generate higher-order composite features, integrating medical prior knowledge. Machine learning models were subsequently developed, and interpretability analyses were performed using SHAP to identify key predictors. </sec> <sec> <title>RESULTS</title> The Random Forest model enhanced by TVAE-based feature augmentation demonstrated the best performance. On the test dataset, it achieved a recall of 0.7559, an accuracy of 0.8444, and an area under the receiver operating characteristic curve (AUROC) of 0.8873. SHAP analysis identified the following five features as the most influential predictors: fasting blood glucose, the composite feature (fasting blood glucose + triglycerides) × pre-pregnancy BMI, activated partial thromboplastin time, leukocyte count, and neutrophil count. </sec> <sec> <title>CONCLUSIONS</title> The proposed dual-dimensional enhancement framework effectively alleviates data limitations and captures complex feature interactions in early GDM prediction. This strategy not only improves model performance, particularly in recall, but also provides interpretable biological evidence to support rapid clinical screening, stratified management, and early intervention in pregnancy. </sec>

Domain Generation Algorithms (DGAs) continue to pose a significant threat in modern malware infrastructures by enabling resilient and evasive communication with Command and Control (C&C) servers. Traditional detection methods—rooted in statistical heuristics, feature engineering, and shallow machine learning—struggle to adapt to the increasing sophistication, linguistic mimicry, and adversarial variability of DGA variants. The emergence of Large Language Models (LLMs) marks a transformative shift in this landscape. Leveraging deep contextual understanding, semantic generalization, and few-shot learning capabilities, LLMs such as BERT, GPT, and T5 have shown promising results in detecting both character-based and dictionary-based DGAs, including previously unseen (zero-day) variants. This paper provides a comprehensive and critical review of LLM-driven DGA detection, introducing a structured taxonomy of LLM architectures, evaluating the linguistic and behavioral properties of benchmark datasets, and comparing recent detection frameworks across accuracy, latency, robustness, and multilingual performance. We also highlight key limitations, including challenges in adversarial resilience, model interpretability, deployment scalability, and privacy risks. To address these gaps, we present a forward-looking research roadmap encompassing adversarial training, model compression, cross-lingual benchmarking, and real-time integration with SIEM/SOAR platforms. This survey aims to serve as a foundational resource for advancing the development of scalable, explainable, and operationally viable LLM-based DGA detection systems.

Contract risk identification is essential for preventing disputes and losses in construction industry. Large language models (LLMs) have achieved substantial impact across a wide range of natural language processing tasks, which presents an opportunity for automating contract review without heavy data processing and feature engineering. However, LLMs still has difficulty in recalling facts while generating knowledge-grounded analysis, especially when related to complex domain knowledge. This paper introduces a Knowledge Graph (KG) modeling approach to enhance the LLM-based automated contract risk identification. A case study demonstrates that our approach exhibits enhanced performance on risk identification tasks compared to non-augmentation scenario.

Plagiarism, the unauthorized reuse of text, fueled by the ease of access to online content, is a pressing concern for academia, publishers, and authors. Paraphrasing, a common tactic in textual plagiarism, compounds the problem further. The automatic detection of paraphrased plagiarism in text documents is a fundamental task in Natural Language Processing (NLP), crucial for maintaining academic integrity and authenticity. This paper presents an extensive investigation into Urdu sentential paraphrased plagiarism detection leveraging advanced Deep Neural Networks (DNNs) and Large Language Models (LLMs). The study builds upon the foundational work and proposes modifications to the Deep Text Reuse and Paraphrased Plagiarism Detection (D-TRaPPD) architecture to incorporate state-of-the-art pre-trained LLMs. The proposed approach, SELLM-D-TRaPPD, integrates various language models, including contextualized sentence embedding-based LLMs, language-agnostic and multilingual transformer-based LLMs, and multilingual knowledge-distilled transformer-based LLMs. We evaluated these models against three benchmark Urdu sentential paraphrase corpora—Urdu Sentential Paraphrase Corpus, Urdu Short Text Reuse Corpus, and Semi-automatic Urdu Sentential Paraphrase Corpus. The results demonstrate the effectiveness of SELLM-D-TRaPPD with LLMs, achieving F1 scores of 92.09%, 96.70%, and 98.23%, respectively. A comparative analysis with existing state-of-the-art methods shows significant performance improvements, establishing SELLM-D-TRaPPD as the new leading approach for Urdu sentential paraphrased plagiarism detection. These findings highlight the value of leveraging advanced neural network architectures and pre-trained LLMs in improving the accuracy and effectiveness of paraphrased plagiarism detection in Urdu, addressing a crucial gap in Urdu NLP research.

Background: The inevitable mistuning of a bladed disk breaks the cyclic symmetry of the structure and can cause vibration localization and amplitude magnification, thereby seriously reducing the structural reliability. Although numerical simulations based on high-fidelity finite element models can precisely calculate the response under some specific mistuning patterns, repeated calculations of random mistuning cases still bring a significant computational burden, even with ad-hoc reduced-order modeling technologies. Objective: In this work, we aim to capture the dynamics of randomly mistuned bladed disks using data-driven surrogate models. Methods: To do that, a physics-informed surrogate model of a designed structural neural network (DS) is proposed, where a “shift” feature from the cyclic symmetry in physics is taken into account for data augmentation. The finite element model of a dummy bladed disk with blade stiffness mistuning is established and taken as an example to test the effectiveness of the surrogate model. Under different training set sizes and excited modes, the performance of the proposed model is compared systematically with a fully connected structural neural network model (FC) and a state-of-the-art deep neural network framework (MS-DNN). Results: The DS model can accurately predict blade displacement for blade-dominated modes with only 600 training data. The R2 value of the displacement response amplitude reaches 0.99956, and the error of the amplitude magnification factor is only 0.68%. The DS model converges faster and has higher prediction accuracy than the FC model. The DS model is superior to the MS-DNN model concerning the generalization ability.

This paper addresses the critical issue of effectiveness, efficiency, and reliability in simulation-based design optimization under surrogate model uncertainty. Specifically, it presents a novel method to build surrogate models iteratively with sufficient fidelity for accurately capturing global optimal design solutions at a minimal cost. The salient feature of the proposed method lies in its unique preference of focusing necessarily high fidelity at potential global optimal regions of surrogate models. The proposed method is the synergic integration of the multiple preference point method, which updates surrogate model at current local optimal points predicted with data-mining techniques in genetic algorithm setup, and the maximum variance point method, which updates surrogate model at the point associated with the maximum prediction variance. Through illustrative comparison studies on thirty different optimization scenarios derived from 15 different test functions, the proposed method demonstrates the tangible reliability advancement. The experimental results indicate that the proposed method can be a reliable updating method in surrogate-model-based design optimization for efficiently locating the global optimal point/points in various kinds of optimization scenarios featured by single/multiple global optimal point/points that may exist at the corners of design space, inside design space, or on the boundaries of design space.

Artificial intelligence-based surrogate modeling for computational cost-effective optimization of hydrogen liquefaction process

Context-augmented convolutional neural networks for twitter sarcasm detection

Variable-fidelity (VF) surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity (HF) simulations with reduced computational power. A key challenge ... | Find, read and cite all the research you need on Tech Science Press

Feature engineering for whole-genome DNA sequences plays a critical role in predicting plant phenotypic traits. However, due to limitations in the models' analytical capabilities and computational resources, the existing methods are predominantly confined to SNP-based approaches, which typically extract genetic variation sites for dimensionality reduction before feature extraction. These methods not only suffer from incomplete locus coverage and insufficient genetic information but also overlook the relationships between nucleotides, thereby restricting the accuracy of phenotypic trait prediction. Inspired by the parallels between gene sequences and natural language, the emergence of large language models (LLMs) offers novel approaches for addressing the challenge of constructing genome-wide feature representations with nucleotide granularity. This study proposes FE-WDNA, a whole-genome DNA sequence feature engineering method, using HyenaDNA to fine-tune it on whole-genome data from 1000 soybean samples. We thus provide deep insights into the contextual and long-range dependencies among nucleotide sites to derive comprehensive genome-wide feature vectors. We further evaluated the application of FE-WDNA in agronomic trait prediction, examining factors such as the context window length of the DNA input, feature vector dimensions, and trait prediction methods, achieving significant improvements compared to the existing SNP-based approaches. FE-WDNA provides a mode of high-quality DNA sequence feature engineering at nucleotide resolution, which can be transformed to other plants and directly applied to various computational breeding tasks.