Knowledge Integration in Differentiable Models: A Comparative Study of Data-Driven, Soft-Constrained, and Hard-Constrained Paradigms for Identification and Control of the Single Machine Infinite Bus System
Korea University（高丽大学）；Hyundai Motor Company（现代汽车公司）
Integrating domain knowledge into neural networks is a central challenge in scientific machine learning. Three paradigms have emerged -- data-driven (Neural Ordinary Differential Equations, NODEs), soft-constrained (Physics-Informed Neural Networks, PINNs), and hard-constrained (Differentiable Programming, DP) -- each encoding physical knowledge at different levels of structural commitment. However, how these strategies impact not only predictive accuracy but also downstream tasks such as control synthesis remains insufficiently understood. This paper presents a comparative study of NODEs, PINNs, and DP for dynamical system modeling, using the Single Machine Infinite Bus power system as a benchmark. We evaluate these paradigms across three tasks: trajectory prediction, parameter identification, and Linear Quadratic Regulator control synthesis. Our results yield three principal findings. First, knowledge representation determines generalization: NODE, which learns the system operator, enables robust extrapolation, whereas PINN, which approximates a solution map, restricts generalization to the training horizon. Second, hard-constrained formulations (DP) reduce learning to a low-dimensional physical parameter space, achieving faster and more reliable convergence than soft-constrained approaches. Third, knowledge fidelity propagates to control performance: DP produces controllers that closely match those obtained from true system parameters, while NODE provides a viable data-driven alternative by recovering control-relevant Jacobians with $3-4\%$ relative error and yielding LQR gains within $0.36\%$ of the ground truth. Based on these findings, we propose a practical decision framework for selecting knowledge integration strategies in neural modeling of dynamical systems.
Comparative Analysis of Polygon-Based and Global Machine Learning Models for Bus Occupancy Prediction
Faculty of Computer and Information Science, University of Ben Gurion, Be'er Sheva, Israel（本·古里安大学计算机与信息科学学院，贝尔谢巴，以色列）；Department of Geography and Environmental Development, Ben-Gurion, Israel（地理与环境发展系，本·古里安，以色列）
News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models
Pegasystems India Worldwide Pvt Ltd（佩加系统印度私人有限公司）；Vardhaman College of Engineering（瓦尔达曼工程学院）；Vasavi College of Engineering（瓦萨维工程学院）；D. K. College（D.K.学院）
Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods
Bucharest University of Economic Studies（布加勒斯特经济研究大学）；Humboldt University of Berlin（柏林洪堡大学）；California Institute of Technology（加州理工学院）；Institute for Economic Forecasting, Romanian Academy（罗马尼亚科学院经济预测研究所）
Understanding the Transit Gap: A Comparative Study of On-Demand Bus Services and Urban Climate Resilience in South End, Charlotte, NC and Avondale, Chattanooga, TN
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models
Department of Electrical Engineering and Computer Science, York University（约克大学电气工程与计算机科学系）；Faculty of Computer Science, Dalhousie University（达尔豪斯大学计算机科学学院）；Cistel Technology（Cistel技术公司）
Classification of HI Galaxy Profiles Using Unsupervised Learning and Convolutional Neural Networks: A Comparative Analysis and Methodological Cases of Studies
Instituto de Astrofísica de Andalucía (IAA-CSIC)（安达卢西亚天体物理研究所（IAA-CSIC））
