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Learning computational fluid dynamics (CFD) traditionally relies on computationally intensive simulations of the Navier-Stokes equations.
A high-order accurate discontinuous finite element method for the numerical solution of the compressible navier–stokes equations
Bassi, F. and Rebay, S · 1996
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Application of cfd to environmental flows
Kim, S.-E. and Boysan, F · 1999
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Computational fluid dynamics study on the performance and mechanism of suction control over a high‐rise building
Zheng, C. and Zhang, Y · 2010
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Heart blood flow simulation: a perspective review
Doost, S. N., Ghista, D., Su, B., Zhong, L., and Morsi, Y. S · 2016
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torchdiffeq, 2018
Chen, R. T. Q · 2018
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Graph Attention Networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Machine learning for fluid mechanics
Brunton, S. L., Noack, B. R., and Koumoutsakos, P · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Learning mesh-based simulation with graph networks
Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., and Battaglia, P · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
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A coupled les-synthetic turbulence method for jet noise prediction
Blake, J. D., Sescu, A., Thompson, D., and Hattori, Y · 2022
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How attentive are graph attention networks?
Brody, S., Alon, U., and Yahav, E · 2022
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Eagle: Large-scale learning of turbulent fluid dynamics with mesh transformers
Janny, S., Benetteau, A., Thome, N., Nadri, M., Digne, J., and Wolf, C · 2023
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Generating images with multimodal language models
Koh, J. Y., Fried, D., and Salakhutdinov, R · 2023
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Scalable transformer for pde surrogate modeling, 2023
Li, Z., Shu, D., and Farimani, A. B · 2023
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Current and emerging deep-learning methods for the simulation of fluid dynamics
Lino, M., Fotiadis, S., Bharath, A. A., and Cantwell, C. D · 2023
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Promptcast: A new prompt-based learning paradigm for time series forecasting, 2023
Xue, H. and Salim, F. D · 2023
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FlashAttention-2: Faster attention with better parallelism and work partitioning
Dao, T · 2024
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Lino, M., Fotiadis, S., Bharath, A. A., and Cantwell, C. D · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R · 2022
Cited alongside, same era.
Learned simulators for turbulence
Stachenfeld, K., Fielding, D. B., Kochkov, D., Cranmer, M., Pfaff, T., Godwin, J., Cui, C., Ho, S., Battaglia, P., and Sanchez-Gonzalez, A · 2022
Cited alongside, same era.
Enhancing computational fluid dynamics with machine learning
Vinuesa, R. and Brunton, S. L · 2022
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Graph learning in physical-informed mesh-reduced space for real-world dynamic systems
Hu, Y., Lei, B., and Castillo, V. M · 2023
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Lstprompt: Large language models as zero-shot time series forecasters by long-short-term prompting, 2024a
Liu, H., Zhao, Z., Wang, J., Kamarthi, H., and Prakash, B. A
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DoRA: Weight-decomposed low-rank adaptation
Liu, S.-Y., Wang, C.-Y., Yin, H., Molchanov, P., Wang, Y.-C. F., Cheng, K.-T., and Chen, M.-H
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Evaluating large language models as virtual annotators for time-series physical sensing data, 2024
Hota, A., Chatterjee, S., and Chakraborty, S · 2024
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Time series forecasting with llms: Understanding and enhancing model capabilities, 2024
Jin, M., Tang, H., Zhang, C., Yu, Q., Liu, C., Zhu, S., Zhang, Y., and Du, M · 2024
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Large language models for time series: A survey
Zhang, X., Chowdhury, R. R., Gupta, R. K., and Shang, J · 2024
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