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In general, large datasets enable deep learning models to perform with good accuracy and generalizability.
Turbulent jet diffusion flames
Bilger, R · 1976
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A numerical study on flame stability at the transition point of jet diffusion flames
Yamashita, H., Shimada, M., and Takeno, T · 1996
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An updated comprehensive kinetic model of hydrogen combustion
Li, J., Zhao, Z., Kazakov, A., and Dryer, F. L · 2004
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A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence
Li, Y., Perlman, E., Wan, M., Yang, Y., Meneveau, C., Burns, R., Chen, S., Szalay, A., and Eyink, G · 2008
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Terascale direct numerical simulations of turbulent combustion using S3D
Chen, J. H., Choudhary, A., de Supinski, B., DeVries, M., Hawkes, E. R., Klasky, S., Liao, W. K., Ma, K. L., Mellor-Crummey, J., Podhorszki, N., Sankaran, R., Shende, S., and Yoo, C. S · 2009
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
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Kaggle: Your machine learning and data science community, 2010
Goldbloom, A. and Hamner, B · 2010
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Image quality metrics: PSNR vs. SSIM
Horé, A. and Ziou, D · 2010
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Class noise vs. attribute noise: A quantitative study
Zhu, X. and Wu, X · 2014
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U-Net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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ImageNet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Deep learning is robust to massive label noise
Rolnick, D., Veit, A., Belongie, S. J., and Shavit, N · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Sun, C., Shrivastava, A., Singh, S., and Gupta, A · 2017
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An efficient transformation scheme for lossy data compression with point-wise relative error bound
Liang, X., Di, S., Tao, D., Chen, Z., and Cappello, F · 2018
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Error-controlled lossy compression optimized for high compression ratios of scientific datasets
Liang, X., Di, S., Tao, D., Li, S., Li, S., Guo, H., Chen, Z., and Cappello, F · 2018
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Exploring the limits of weakly supervised pretraining
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., and van der Maaten, L · 2018
Combustion regime identification from machine learning trained by Raman/Rayleigh line measurements
Wan, K., Hartl, S., Vervisch, L., Domingo, P., Barlow, R. S., and Hasse, C · 2020
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Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE
Abratenko, P., Alrashed, M., An, R., Anthony, J., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Basque, V., et al · 2021
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On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al · 2021
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Detection of precursors of combustion instability using convolutional recurrent neural networks
Cellier, A., Lapeyre, C., Öztarlik, G., Poinsot, T., Schuller, T., and Selle, L · 2021
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Data-assisted combustion simulations with dynamic submodel assignment using random forests
Chung, W. T., Mishra, A. A., Perakis, N., and Ihme, M · 2021
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Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
Wu, J.-L., Xiao, H., and Paterson, E · 2018
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A strategy to apply machine learning to small datasets in materials science
Zhang, Y. and Ling, C · 2018
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Use cases of lossy compression for floating-point data in scientific data sets
Cappello, F., Di, S., Li, S., Liang, X., Gok, A. M., Tao, D., Yoon, C. H., Wu, X.-C., Alexeev, Y., and Chong, F. T · 2019
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Deep learning for in situ data compression of large turbulent flow simulations
Glaws, A., King, R., and Sprague, M · 2020
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Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers
Khan, A., Huerta, E., and Das, A · 2020
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Extraction of mechanical properties of materials through deep learning from instrumented indentation
Lu, L., Dao, M., Kumar, P., Ramamurty, U., Karniadakis, G. E., and Suresh, S · 2020
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On the flame stabilization of turbulent lifted hydrogen jet flames in heated coflows near the autoignition limit: A comparative DNS study
Jung, K. S., Kim, S. O., Lu, T., Chen, J. H., and Yoo, C. S · 2021
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Physics-informed machine learning
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., and Yang, L · 2021
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Pervasive label errors in test sets destabilize machine learning benchmarks
Northcutt, C., Athalye, A., and Mueller, J · 2021
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Semantic segmentation of radio-astronomical images
Pino, C., Sortino, R., Sciacca, E., Riggi, S., and Spampinato, C · 2021
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Blastnet simulation dataset, 2022
Chung, W. T., Jung, K. S., Chen, J. H., Ihme, M., Guo, J., Brouzet, D., and Talei, M · 2022
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Cantera: An object-oriented software toolkit for chemical kinetics, thermodynamics, and transport processes, 2022
Goodwin, D. G., Moffat, H. K., Schoegl, I., Speth, R. L., and Weber, B. W · 2022
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Combustion machine learning: Principles, progress and prospects
Ihme, M., Chung, W. T., and Mishra, A. A · 2022
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A roadmap for big model
Yuan, S., Zhao, H., Zhao, S., Leng, J., Liang, Y., Wang, X., Yu, J., Lv, X., Shao, Z., He, J., et al · 2022
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