Agusty, A. I., & Chen, H.-L. (2026). The Effects of an SVVR-Based argumentation flipped learning approach on students’ physics learning achievement, argumentative writing, classroom engagement, and perceptions. Journal of Science Education and Technology, 35(2), 580–600. https://doi.org/10.1007/s10956-025-10268-3

Babalola, F. E., Fakoyede, S. J., Ojobola, F. B., Ayodeji, M. A., Ekundayo, S. K., & Alabi, D. O. (2026). Evaluating the effectiveness of multimedia integration in enhancing physics instruction in secondary schools. Discover Education, 5(1). https://doi.org/10.1007/s44217-025-00871-2

Chang, J., Zhao, H., & Zhang, L. (2026). Beyond semantic understanding: physics-informed adaptation of video foundation models for precision industrial metrology. ICMR 2026 - Proceedings of the 16th ACM International Conference on Multimedia Retrieval, 2704–2713. https://doi.org/10.1145/3805622.3810652

Chaurasia, R., & Tripathi, N. (2026). Popularizing science literacy among the internet users: an empirical study on comprehension and reception of science explainer videos on social media. International Journal of Media and Information Literacy, 11(1), 3–13. https://doi.org/10.13187/ijmil.2026.1.3

Chen, G. (2026). Trajectory and landing point analysis of tennis based on improved real-time object detection algorithm. Discover Artificial Intelligence, 6(1). https://doi.org/10.1007/s44163-026-01127-0

Chiha, Z., Péteri, R., & Mascarilla, L. (2026). Physics-informed neural networks and monocular vision for kinematics analysis: Application to table tennis. Signal Processing: Image Communication, 147. https://doi.org/10.1016/j.image.2026.117587

Deb, N., & Rahman, T. (2026). AgriGen-XL: a physics-aware multi-scale generative framework integrating EfficientNet, DeepLabV3+, and YOLOv5 for precision agricultural imaging. Discover Artificial Intelligence, 6(1). https://doi.org/10.1007/s44163-026-01070-0

Eadkhong, T., Channuie, P., Matan, N., & Dam-O, P. (2026). Enhancing engineering students’ understanding of fluid flow using motion analysis software: a lab@home-inspired contemporary physics laboratory. Computer Applications in Engineering Education, 34(3). https://doi.org/10.1002/cae.70202

Goranov, V., Shelyakova, T., Koštál, J., Makhaniok, A., Giavaresi, G., & Dediu, V. A. (2026). Advanced mathematical platform for the control and manipulation of magnetized living cells. Bioengineering, 13(5). https://doi.org/10.3390/bioengineering13050560

Han, Y., Feng, Z., Sun, J., & Yang, X. (2026). Recent advances in multimode fiber computational imaging: from transmission-matrix models to physics-informed deep learning (cover paper·invited). Infrared and Laser Engineering, 55(6). https://doi.org/10.3788/IRLA20260012

Hu, X., Xing, Z., Wang, T., Fu, C.-W., & Heng, P.-A. (2026). Unveiling deep shadows: a survey and benchmark on image and video shadow detection, removal, and generation in the deep learning era. International Journal of Computer Vision, 134(4). https://doi.org/10.1007/s11263-026-02744-z

Huo, J., Wang, J., She, R., & Lu, Y. (2026). Video-rate terahertz single-pixel imaging via physics-enhanced deep learning and VCSEL array modulation. APL Photonics, 11(6). https://doi.org/10.1063/5.0324073

Ince. (2026). Corrigendum to “Physics-informed deep neural network framework for prediction of fatigue crack growth in LPBF-manufactured metallic alloys”(International Journal of Fatigue, 2026), 208, C, (109522), (S0142112326000423), 10.1016/j.ijfatigue.2026.109522. International Journal of Fatigue, 208. https://doi.org/10.1016/j.ijfatigue.2026.109568

Jin, Y., Zhu, Z., Xue, X., Fu, G., Zhang, C., & Li, Y. (2026). Video-Based rainfall opportunistic sensing in hydrology: a lightweight machine learning approach. Water Resources Research, 62(7). https://doi.org/10.1029/2025WR041953

Jutkowitz, R., Asterhan, C. S. C., Lehavi, Y., & Merzel, A. (2026). Preparing for peer-led productive professional discourse around classroom videos: In- and pre-service teachers. Teaching and Teacher Education, 175. https://doi.org/10.1016/j.tate.2026.105441

Kim, J., & Yu, R. (2026). Baseball pitching motion reconstruction from single-view videos. Visual Computer, 42(9). https://doi.org/10.1007/s00371-026-04603-x

Li, R., Tang, L., & Feng, H. (2026). From vision to physics: Generative occupant modeling and 3D spatial data architecture in indoor environments. Energy and Buildings, 367. https://doi.org/10.1016/j.enbuild.2026.117785

Li, Y., Wang, X., Song, X., Fan, C., Zhu, B., Peng, J., Liang, Q., Lam, J., Sze, K.-Y., & Kwok, K.-W. (2026). Graph neural cloth: a graph-neural-network-based framework for non-skinning cloth simulation. Advanced Intelligent Systems, 8(4). https://doi.org/10.1002/aisy.202501120

Lianhui, Z., Jingjing, Z., Zhongjian, G., Junwu, L., & Bingyang, L. (2026). Turbulent image non-blind restoration method with differential edge prior fusion. Laser and Optoelectronics Progress, 63(12). https://doi.org/10.3788/LOP252579

Lin, W., Zhou, A., Kuo, H.-C., Chan, A. B., You, J., Jen, A. K. Y., & Ma, W.-Y. (2026). Physics-informed weakly supervised learning for in-situ photoluminescence dynamics analysis in perovskite solar cells. Nano Energy, 151. https://doi.org/10.1016/j.nanoen.2026.111831

Lu, N., & Pan, M. (2026). CausalGS: Learning Physical Causality of 3D dynamic scenes with gaussian representations. ICMR 2026 - Proceedings of the 16th ACM International Conference on Multimedia Retrieval, 845–854. https://doi.org/10.1145/3805622.3810866

Mohamad, F., Zulkernine, F., & Sears, K. (2026). PhysQual: A quality-aware learning framework for robust remote photoplethysmography. Smart Health, 41. https://doi.org/10.1016/j.smhl.2026.100683

Mu, Y., Zhang, Z., Shi, Y., Yang, D., Matsumoto, M., Imamura, K., Tevet, G., Guo, C., Taylor, M., Shu, C., Xi, P., & Peng, X. Bin. (2026). SMP: Reusable score-matching motion priors for physics-based character control. ACM Transactions on Graphics, 45(4). https://doi.org/10.1145/3811282

Nasbey, H., Fitri, U. R., & Yulianawati, A. (2026). Audio-visual based physics learning media: Songs and video clips as tools to simplify understanding of temperature and heat. AIP Conference Proceedings, 3332(1). https://doi.org/10.1063/5.0333608

Oishi, N., Birch, P., Roggen, D., & Lago, P. (2026). Physically plausible data augmentations for wearable imu-based human activity recognition using physics simulation. IEEE Sensors Journal, 26(7), 10445–10457. https://doi.org/10.1109/JSEN.2026.3661047

Punnakkal, A. R., Jadhav, S. S., Celeste, A. V., Horsch, A., Agarwal, K., & Prasad, D. K. (2026). 3D Mitochondria Shape Library for Optical Microscopy (3DMSL): A multimodal dataset for deep learning based mitochondrial analysis. Data in Brief, 65. https://doi.org/10.1016/j.dib.2026.112507

Qizhen, Z., Qian, Z., Jiahui, Z., Wentao, S., Manting, Z., Man, D., Kele, C., Xin, T., Kaiyu, C., Lanqiang, Z., Youming, G., & Changhui, R. (2026). Research progress on data-driven adaptive optics for high-resolution imaging (Invited). Laser and Optoelectronics Progress, 63(8). https://doi.org/10.3788/LOP252450

Rismawati, E., Purqon, A., Wahyu, S., & Purwalaksana, A. Z. (2026). Investigation of Thermal Effects and Moisture Content on the Aerodynamic Properties of Pinus merkusii Seeds Using Video Tracker Analysis. AIP Conference Proceedings, 3433(1). https://doi.org/10.1063/5.0338662

Romadhon, D. R., Wijaya, A. I., Nurlaela, A., & Muliyah, E. (2026). Digital equity in physics education: a mixed-methods study of tiktok microlearning. AIP Conference Proceedings, 3433(1). https://doi.org/10.1063/5.0337944

Sha, B. B., Thakare, K. V., Kar, S., Dogra, D. P., & Das, M. K. (2026). Machine learning-based heat flux estimation from high-speed video during saturated pool boiling over vertical tube. Scientific Reports, 16(1). https://doi.org/10.1038/s41598-026-35147-8

Song, Z., Li, Q., Qin, S., Chen, Y., Chen, T., Lin, L., & Wang, G. (2026). Learning physics from pretrained video models: a multimodal continuous and sequential world interaction models for robotic manipulation. ICMR 2026 - Proceedings of the 16th ACM International Conference on Multimedia Retrieval, 758–767. https://doi.org/10.1145/3805622.3810752

Srinivasan, Dhavale, S., & Agrawal, P. (2026). Deep learning–based automated aero-engine blade inspection, damage tracking, and maintenance decision support. Aerospace Science and Technology, 178. https://doi.org/10.1016/j.ast.2026.112350

Stolzenberger, C., Frank, F., & Trefzger, T. (2026). Theory meets real-life experiment - understanding electric circuits with the ‘PUMA : Spannungslabor’ AR-app. Physics Education, 61(4). https://doi.org/10.1088/1361-6552/ae7e34

Wang, S., Dou, Z., Shan, S., Liu, T.-R., & Lu, L. (2026). FunDiff: diffusion models over function spaces for physics-informed generative modeling. Nature Communications , 17(1). https://doi.org/10.1038/s41467-026-72292-0

Xiao, H. (2026). Automatic Construction Method of Personalized Educational Resources Based on Intelligent Optimization. In Frontiers in Artificial Intelligence and Applications (Vol. 421). https://doi.org/10.3233/FAIA260451

Youlanda, A. T., Sari, A. F., Abida, S. N., Suprapto, N., Lestari, N. A., & Hidaayatullaah, H. N. (2026). Inspiring physics education to grow students’ interest through collaborative innovation and interactive media. AIP Conference Proceedings, 3332(1). https://doi.org/10.1063/5.0328738

Zhang, J., Jafary-Zadeh, M., Laskowski, R., Choo, Y. Y. D., & Sing, S. L. (2026). Insights beneath the surface: Physics-guided multi-task learning for in situ defect detection in laser powder bed fusion. Journal of Manufacturing Processes, 169, 126–136. https://doi.org/10.1016/j.jmapro.2026.04.032

Zhang, X., Wang, X., & Fan, X. (2026). Bridging the visual-to-physical gap: physically aligned representations for fall risk analysis. Frontiers in Medicine, 13. https://doi.org/10.3389/fmed.2026.1837781

Zhong, C., & Xu, C. (2026). Thermal4D: Physics-Driven gaussian splatting for dynamic thermal scene reconstruction. Sensors, 26(10). https://doi.org/10.3390/s26103041

Zhu, J., Cai, W., Zhang, M., Wu, W., Ma, Y., & Mo, Y. (2026). PhysTrack: Physics-driven multi-AUV tracking from UAV imagery via pose estimation and dynamic state inference. Ocean Engineering, 358(P1). https://doi.org/10.1016/j.oceaneng.2026.125692