2025年12月1日 · A machine learning framework, which was designed to simulate, predict, and optimize the entrained-flow coal gasification process, has been presented and validated in the present study.

2024年10月1日 · The proposed physical simulation-machine learning model in this study enables inexpensive and accurate optimization of DED-LB process parameters.

To map the evidence of the simulation debriefing phase in simulation activities of nursing education, to address and inform clinical teaching and learning in nursing. A scoping review. A systematic review …

2025年2月11日 · Article Open access Published: 11 February 2025 Optimizing casting process using a combination of small data machine learning and phase-field simulations Xiaolong Pei, Jiaqi Pei, Hua …

2025年4月1日 · Although facilitation in simulation-based education can encompass many different methodologies, limited data exists on the outcomes associated with all components of facilitation. …

2024年8月1日 · In this work, the strategy of machine learning-assisted L-PBF process model construction is shown in Fig. 1, which aims to quickly select the optimal parameters for finite element …

2020年8月4日 · As there inevitably is model-process mismatch, we further apply transfer learning approach to reduce the discrepancies between the simulation and physical domains. This approach …

The following are the components of CISES. Simulation Mapping Integrating simulation into the curriculum began with developing a curriculum map to determine its placement in the clinical courses …

2025年9月1日 · Effects of La on the clustering process of B2-NiAl nanoparticles in maraging steels: Atomic-scale characterization and molecular dynamics simulation using deep learning potential

2024年6月1日 · Although deep learning (DL) models, especially long-short-term memory (LSTM), demonstrate greater accuracy than process-based models in rainfall-runoff simulation, the …