Enhancing deep learning-based field reconstruction with a differentiable learning framework

Wang, M. & Zaki, TA estimate a condition in the flowing channel flow from limited notes. J. Fluid mech. 917A9 (2021).
Malekloo, A., Ozer, E., Alhamaydeh, M. & GIROLAMI, M. Machine Learning and Health Health Overview with textology technology and hight-remarnal Source Perffects. structure. Health screen. 211906-1955 (2022).
The scientific researcher from Google
Thelen, A. Et al. Comprehensive review of digital twins – Part One: Modeling and empowerment technologies. structure. Multidis. optimum. 65354 (2022).
The scientific researcher from Google
Feizi, N., Patel, RV, Kermani, MR & Atashzar, SF Adaptive Wave RecONSTRUTUTURC through BMFLC, a transparent transparency organization on late networks. IEEE Trans. Robot. 382928-2942 (2022).
The scientific researcher from Google
Dong, C., LOY, CC, He, K. & Tang, X. Image Super-Desology using deep fodder networks. IEEE Trans. Anal pattern. Mach. Minds. 38295-307 (2015).
The scientific researcher from Google
Lecun, Y., Bengio, Y. & Hinton, G. Deep Learning. NATURE 521436-444 (2015).
The scientific researcher from Google
BRUNTON, SL, Noack, Br & Koumoutsakos, P. Automated Liquid Mechanics. Annu. Priest mechanical fluid. 52477-508 (2020).
Fukami, K., Fukagata, K. & Taira, K. RECONDRUCTION SUPER-RESOLUTION for turbulent flows with machine learning. J. Fluid mech. 870106-120 (2019).
Gümemes, A., Sanmiguel VILA, C. & Discetti, S. Ultra -accurate aggressive networks of randomly classified fields. Nat. Mach. Minds. 41165-1173 (2022).
The scientific researcher from Google
Sha, Y., Xu, Y., Wei, Y. & Wang, C. Predicting List Presse on Hydrovotovovs on the basis of improved compressed sensor technology. physics. Liquid 36013321 (2024).
Peng, X., LI, X., Gong, Z., Zhao, X. & YAO, W. A deep educational method based on partition modeling to rebuild the temperature field. int. J. Thermal Sci. 182107802 (2022).
The scientific researcher from Google
Fukami, K., AN, B., Nohmi, M. J. Liquids Engineer. 144121501 (2022).
The scientific researcher from Google
Zhong, Y., Fukami, K., AN, B. & Taira, K. RecONSTRUCTION SENSOR SENSOR OF Fortex-Passing with Automated Learning. theory. account. Dyn Fluid. 37269-287 (2023).
The scientific researcher from Google
Shiri, FM, Perumal, T J. artif. Minds. 6301-360 (2024).
The scientific researcher from Google
He, X., Wang, Y. & LI, J physics. Liquid 34087114 (2022).
Ogoke, F., Meidani, K., HASHEMI, A. & Farimani, AB Graph Networks Letows applied to the non -structural flow field data. Mach. Learn: Sci. technique. 2045020 (2021).
The scientific researcher from Google
Bhatti, UA, Tang, H., Wu, G., Marjan, S. & Hussain, A. Deep Learning with Graphic Drawing Networks: Overview and Latest Applications in Mass Intelligence. int. C. split. 20238342104 (2023).
The scientific researcher from Google
Liang, S. And others. Engn: High Production and Energy Power accelerator for large graphic nerve networks. IEEE Trans. account. 701511-1525 (2020).
The scientific researcher from Google
Azzzadnesheli, K. et al. Nervous operators to accelerate scientific simulation and design. Nat. Reverend Viz. 6320-328 (2024).
Zhao, x. et al. RECFNO: The method of flowing and flowing variable and heat is a notes scattered through a nervous foulee operator. int. J. Thermal Sci. 195108619 (2024).
The scientific researcher from Google
Fukami, K. et al. Rebuilding the global field from scattered sensors with deep learning with the help of mosaics. Nat. Mach. Minds. 3945-951 (2021).
The scientific researcher from Google
Santos, G and others. Senseiver Development to rebuild the effective field of scattered notes. Nat. Mach. Minds. 51317-1325 (2023).
The scientific researcher from Google
Li, B., LIU, H. & Wang, R. Apply an effective sensor to rebuild the sign based on the luser methods. IEEE Trans. Signal process. 691885-1898 (2021).
Manuar, K. IEEE Control Syst. magazine 3863-86 (2018).
Nagata, T. et al. How to choose a data -based sensor based on the near -dimensional data improvement with associated measurement noise. IEEE Trans. Signal process. 705251-5264 (2022).
Nagata, T., Yamada, K., Nakai, K., Saito, Y. & Nonomura, T IEEE J. 239536-9548 (2023).
Koo, B., Son, H., Kim, H., Jo, T Appl. Thermal engineer. 173115153 (2020).
The scientific researcher from Google
Zhang, Z., Peng, C., Wang, G., Ju, Z. & MA, L. Mechanical. split. Signal process. 195110319 (2023).
The scientific researcher from Google
Erichson, NB et al. Shallow nervous networks to rebuild fluid flow with limited sensors. Brook. R. Suk. A 47620200097 (2020).
Dubois, P., GOMEZ, T., Planckaert, L. & Perret, L. Automated Learning to rebuild fluid flow from limited measurements. J. Comput. physics. 448110733 (2022).
Marcato, A., O’Malley, D., Viswanathan, H., Guiltinan, E. & Santos, Je Recondruption of Fields from Sparse Sensing: Mostor Sensor Inportible enhances generalization. Preprint at https://arxiv.org/abs/2312.09176 (2023).
Marcato, A. Et al. A journey above the destination: the dynamic sensor mode enhances the circular. Mach. Learn: Sci. technique. 5025070 (2024).
The scientific researcher from Google
Padhma, M. A comprehensive introduction to evaluating slope models. Blogathon Data Science (2023).
Clark, E., Brunton, SL & Kutz, JN Multi-FIDELILITY SENSOR SELECTION: Greed algorithms to put cheap and costly sensors with cost restrictions. IEEE J. 21600-611 (2020).
The scientific researcher from Google
Li, B., LIU, H. & Wang, R. Data -based sensor to rebuild the effective thermal field. Sci. China technology. Sci. 641981-1994 (2021).
The scientific researcher from Google
Vasay, Mac and others. A your-The evolutionary improvement algorithm based on the correlation between the two stakeholders to collect pollution events and put sensors in water distribution systems. J. Cleaner prod. 319128763 (2021).
The scientific researcher from Google
Colonius, T account. Appl methods. Mechanical. Engineer. 1972131-2146 (2008).
Fukagata, K., Kasagi, N. & Koumoutsakos, P. Theoretical Prediction to reduce the friction with the turbulent flow by super surfaces. physics. Liquid 18051703 (2006).
Zhao, X., Chen, X., Gong, Z., Yao, W. & Zhang, Y. A hybrid method that depends on the appropriate orthogonal decomposition and deep nerve networks to rebuild the thermal field. Syst expert. Appl. 247123137 (2024).
The scientific researcher from Google
Zhang, X., Ji, T., XIE, F., Zheng, H. & Zheng, Y. Unstable Frocification of Instantial Flowing from Sensors scattered by compressed sensing low -arranged modeling. account. Appl methods. Mechanical. Engineer. 393114800 (2022).
Wu, J., Xiao, D. & Luo, MM Bearning Deeped Deled Order Model to predict a high -dimensional flow of scattered data. physics. Liquid 35103115 (2023).
Lu, L., Jin, P., Pang, G., Zhang, Z. & Karniadakis, Gerning Operators via Deeponet based on the theory of global approaching operators. Nat. Mach. Minds. 3218-229 (2021).
The scientific researcher from Google
For me, g. And others. Foreeh, the nervous operator of partial partial differential equations. in Brook. From ICLR 2021 (2021); https://openreview.net/pdf/53c47f849d1cd4d21B865CAF7D74E07A5C42AAA4.PDF
Pata, J., Duarte, J. euro. physics. C. C 81381 (2021).
The scientific researcher from Google
LIU, x. et al. DSPO data collection. Zenudo https://zenodo.org/RCords/15259970 (2025).
LIU, x. et al. DSPO: DSPO-V1. Zenudo https://doi.org/10.5281/zenodo.15259928 (2025).
LIU, x. et al. DSPO codebase. CodeoOCEAN https://doi.org/10.24433/co.4342598.v2 (2025).
Don’t miss more hot News like this! AI/" target="_blank" rel="noopener">Click here to discover the latest in AI news!
2025-07-22 00:00:00