[MRO20] D. Maoutsa, S. Reich, and M. Opper. "Interacting Particle Solutions of Fokker-Planck Equations Through Gradient-Log-Density Estimation". In: Entropy 22.8 (2020), p. 802. DOI: 10.3390/e22080802. URL: https://doi.org/10.3390/e22080802.
[Øks03] B. Øksendal. "Stochastic differential equations". In: Stochastic differential equations. Springer, 2003, pp. 65-84.
[Pap+21] G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan. "Normalizing flows for probabilistic modeling and inference". In: The Journal of Machine Learning Research 22.1 (2021), pp. 2617-2680.
[Par21] É. Pardoux. Stochastic Partial Differential Equations: An Introduction. Springer, 2021.
[Pfa+20] T. Pfaff, M. Fortunato, A. Sanchez-Gonzalez, and P. Battaglia. “Learning mesh-based simulation with graph networks”. In: International Conference on Learning Representations (2020).
[Ram+20] Z. Ramzi, B. Remy, F. Lanusse, J. Starck, and P. Ciuciu. "Denoising Score-Matching for Uncertainty Quantification in Inverse Problems". In: CoRR abs/2011.08698 (2020). URL: https://arxiv.org/abs/2011.08698.
[SC20] S. Schoenholz and E. D. Cubuk. "Jax md: a framework for differentiable physics". In: Advances in Neural Information Processing Systems 33 (2020), pp. 11428-11441.
[SE19] Y. Song and S. Ermon. "Generative Modeling by Estimating Gradients of the Data Distribution". In: Advances in Neural Information Processing Systems. 2019, pp. 11895-11907. URL: https : / / proceedings . neurips . cc / paper / 2019 / hash / 3001ef257407d5a371a96dcd947c7d93-Abstract.html.
[SLG22] C. Salvi, M. Lemercier, and A. Gerasimovics. "Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamics". In: NeurIPS. 2022. URL: http://papers.nips.cc/paper%5C_files/paper/2022/bitical/091166620a04a289c555f411d8899049-Abstract-Conference.html.
[Soh+15] J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli. “Deep Unsupervised Learning using Nonequilibrium Thermodynamics”. In: Proceedings of the 32nd International Conference on Machine Learning. Vol. 37. JMLR Workshop and Conference Proceedings. JMLR.org, 2015, pp. 2256–2265. URL: http://proceedings.mlr.press/v37/sohl-dickstein15.html.
[Son+19] Y. Song, S. Garg, J. Shi, and S. Ermon. "Sliced Score Matching: A Scalable Approach to Density and Score Estimation". In: Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence. Vol. 115. Proceedings of Machine Learning Research. AUAI Press, 2019, pp. 574-584. URL: http://proceedings.mlr.press/v115/song20a.html.
[Son+21a] Y. Song, C. Durkan, I. Murray, and S. Ermon. "Maximum Likelihood Training of Score-Based Diffusion Models". In: Advances in Neural Information Processing Systems. 2021, pp. 1415-1428. URL: https://proceedings.neurips.cc/paper/2021/ hash/0a9fdbb17feb6ccb7ec405cfb85222c4-Abstract.html.
[Son+21b] Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, et al. "Score-Based Generative Modeling through Stochastic Differential Equations". In: 9th International Conference on Learning Representations. OpenReview.net, 2021. URL: https://openreview.net/forum?id=PxTIG12RRHS.
[Son+22] Y. Song, L. Shen, L. Xing, and S. Ermon. "Solving Inverse Problems in Medical Imaging with Score-Based Generative Models". In: The Tenth International Conference on Learning Representations. OpenReview.net, 2022. URL: https://openreview.net/forum?id=vaRCHVjOuGI.
[Sta+21] K. Stachenfeld, D. B. Fielding, D. Kochkov, M. Cranmer, et al. "Learned Simulators for Turbulence". In: International Conference on Learning Representations. 2021.
[Thu+21] N. Thuerey, P. Holl, M. Mueller, P. Schnell, et al. Physics-based Deep Learning. WWW, 2021. URL: https://physicsbaseddeeplearning.org.
[Tom+17] J. Tompson, K. Schlachter, P. Sprechmann, and K. Perlin. "Accelerating Eulerian Fluid Simulation With Convolutional Networks". In: 5th International Conference on Learning Representations, Workshop Track Proceedings. OpenReview.net, 2017. URL: https://openreview.net/forum?id=ByH2gxrKl.