[12] Julian Mack et al. "Attention-based convolutional autoencoders for 3d-variational data assimilation". In Computer Methods in Applied Mechanics and Engineering 372 (2020). URL: https://arxiv.org/abs/2101.02121.
[13] Rossella Arcucci et al. "Deep data assimilation: integrating deep learning with data assimilation". In Applied Sciences 11.3 (2021), p. 1114. URL: https://www.mdpi.com/2076-3417/11/3/1114.
[14] Thomas Frerix et al. "Variational data assimilation with a learned inverse observation operator". In International Conference on Machine Learning. 2021, pp. 3449-3458. URL: https://arxiv.org/abs/2102.11192.
[15] Ronan Fablet et al. "Learning variational data assimilation models and solvers". In Journal of Advances in Modeling Earth Systems 13.10 (2021). URL: https://arxiv.org/abs/2007.12941.
[16] Yu-Hong Yeung et al. "Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems". In Water Resources Research 58.5 (2022). URL: https://arxiv.org/abs/2108.00037.
[17] Julien Brajard et al. "Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model". In Journal of computational science 44 (2020). URL: https://arxiv.org/abs/2001.01520.
[18] Julien Brajard et al. "Combining data assimilation and machine learning to infer unresolved scale parametrization". In Philosophical Transactions of the Royal Society A 379.2194 (2021). URL: https://arxiv.org/abs/2009.04318.
[19] Kai Zhang et al. "Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies". In *Petroleum Science* 19.2 (2022), pp. 707-719.
[20] Robin Rombach et al. "High-resolution image synthesis with latent diffusion models". In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. URL: https://arxiv.org/abs/2112.10752.
[21] Aditya Ramesh et al. "Hierarchical text-conditional image generation with clip latents". In (2022). URL: https://arxiv.org/abs/2204.06125.
[22] Chitwan Sahara et al. "Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding". In Advances in Neural Information Processing Systems. 2022. URL: https://openreview.net/forum?id=08Yk-n512A1.
[23] Jonathan Ho et al. "Video Diffusion Models". In ICLR Workshop on Deep Generative Models for Highly Structured Data. 2022. URL: https://openreview.net/forum?id=BBe1R2NdDZ5.
[24] Jonathan Ho et al. " Cascaded Diffusion Models for High Fidelity Image Generation". In Journal of Machine Learning Research 23.47 (2022), pp. 1-33. URL: http://jmlr.org/papers/v23/21-0635.
[25] Jonathan Ho et al. "Imagen video: High definition video generation with diffusion models". 2022. URL: https://arxiv.org/abs/2210.02303.
[26] Zhifeng Kong et al. "DiffWave: A Versatile Diffusion Model for Audio Synthesis". In International Conference on Learning Representations. 2021. URL: https://openreview.net/forum?id=a-xFK8Ymz5J.
[27] Karan Goel et al. "It's raw! audio generation with state-space models". In International Conference on Machine Learning. PMLR. 2022. URL: https://proceedings.mlr.press/v162/goel122a.
[28] Yang Song et al. "Score-Based Generative Modeling through Stochastic Differential Equations". In International Conference on Learning Representations. 2021. URL: https://openreview.net/forum?id=PxTIG12RRHS.
[29] Simo Särkkä and Arno Solin. “Applied stochastic differential equations”. Vol. 10. Cambridge University Press, 2019.