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We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling.
Sur la théorie relativiste de l’électron et l’interprétation de la mécanique quantique
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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Regularization under diffusion and anticoncentration of the information content
Eldan, R. and Lee, J. R · 2018
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Stochastic adversarial video prediction
Lee, A. X., Zhang, R., Ebert, F., Abbeel, P., Finn, C., and Levine, S · 2018
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Unterthiner, T., Van Steenkiste, S., Kurach, K., Marinier, R., Michalski, M., and Gelly, S · 2018
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Tzen, B. and Raginsky, M · 2019
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Yi, K., Gan, C., Li, Y., Kohli, P., Wu, J., Torralba, A., and Tenenbaum, J. B · 2019
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Chen, R. T., Amos, B., and Nickel, M · 2020
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Building normalizing flows with stochastic interpolants
Albergo, M. S. and Vanden-Eijnden, E · 2022
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Learning to correct spectral methods for simulating turbulent flows
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Flow matching for generative modeling
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Flow straight and fast: Learning to generate and transfer data with rectified flow
Liu, X., Gong, C., and Liu, Q · 2022
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Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., Azizzadenesheli, K., et al · 2022
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Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., and Anandkumar, A · 2020
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Oprea, S., Martinez-Gonzalez, P., Garcia-Garcia, A., Castro-Vargas, J. A., Orts-Escolano, S., Garcia-Rodriguez, J., and Argyros, A · 2020
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Training neural operators to preserve invariant measures of chaotic attractors
Jiang, R., Lu, P. Y., Orlova, E., and Willett, R · 2023
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From zero to turbulence: Generative modeling for 3d flow simulation
Lienen, M., Lüdke, D., Hansen-Palmus, J., and Günnemann, S · 2023
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Non-denoising forward-time diffusions
Peluchetti, S · 2023
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Bayesian learning via neural schrödinger–föllmer flows
Vargas, F., Ovsianas, A., Fernandes, D., Girolami, M., Lawrence, N. D., and Nüsken, N · 2023
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Data-driven probability density forecast for stochastic dynamical systems
Zhao, M. and Jiang, L · 2023
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Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
Ma, N., Goldstein, M., Albergo, M. S., Boffi, N. M., Vanden-Eijnden, E., and Xie, S · 2024
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Dyffusion: A dynamics-informed diffusion model for spatiotemporal forecasting
Rühling Cachay, S., Zhao, B., Joren, H., and Yu, R · 2024
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Diffusion schrödinger bridge matching
Shi, Y., De Bortoli, V., Campbell, A., and Doucet, A · 2024
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