[And82] B. Anderson. "Reverse-time diffusion equation models". In: Stochastic Processes and their Applications 12.3 (1982), pp. 313-326.
[Bar+19] Y. Bar-Sinai, S. Hoyer, J. Hickey, and M. P. Brenner. "Learning data-driven discretizations for partial differential equations". In: Proceedings of the National Academy of Sciences 116.31 (2019), pp. 15344-15349.
[BLL02] X. Blanc, C. Le Bris, and P.-L. Lions. “From molecular models to continuum mechanics”. In: Archive for Rational Mechanics and Analysis 164.4 (2002), pp. 341–381.
[BWW22] J. Brandstetter, D. Worrall, and M. Welling. "Message passing neural PDE solvers". In: International Conference on Learning Representations (2022).
[Che+18a] R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud. "Neural ordinary differential equations". In: Advances in neural information processing systems 31 (2018).
[Che+18b] T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. Duvenaud. "Neural Ordinary Differential Equations". In: Advances in Neural Information Processing Systems. 2018, pp. 6572-6583. URL: https://proceedings.neurips.cc/paper/2018/bitstream/69386f6bb1dfed68692a24c8686939b9-Abstract.html.
[Che+21] N. Chen, Y. Zhang, H. Zen, R. J. Weiss, et al. "WaveGrad: Estimating Gradients for Waveform Generation". In: 9th International Conference on Learning Representations. OpenReview.net, 2021. URL: https://openreview.net/forum?id=NsMLjcFa080.
[Chu+22] H. Chung, B. Sim, D. Ryu, and J. C. Ye. "Improving Diffusion Models for Inverse Problems using Manifold Constraints". In: NeurIPS. 2022. URL: http://papers.nips.cc/paper/2022/hash/a48e5877c7bf86a513950ab23b360498 - Abstract - Conference.html.
[Chu+23] H. Chung, J. Kim, M. T. McCann, M. L. Klasky, and J. C. Ye. "Diffusion Posterior Sampling for General Noisy Inverse Problems". In: The Eleventh International Conference on Learning Representations. OpenReview.net, 2023. URL: https://openreview.net/pdf?id=0nD9zGAGT0k.
[CSY22] H. Chung, B. Sim, and J. C. Ye. “Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction”. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022. IEEE, 2022, pp. 12403-12412. DOI: 10.1109/CVPR52688.2022.01209. URL: https://doi.org/10.1109/CVPR52688.2022.01209.
[Del+18] S. Delaquis, M. Jewell, I. Ostrovskiy, M. Weber, et al. "Deep neural networks for energy and position reconstruction in EXO-200". In: Journal of Instrumentation 13.08 (2018), P08023.
[DN21] P. Dhariwal and A. Q. Nichol. "Diffusion Models Beat GANs on Image Synthesis". In: Advances in Neural Information Processing Systems. 2021, pp. 8780-8794. URL: https://proceedings.neurips.cc/paper/2021/bit/49ad23d1ec9fa4bd8d77d02681df5cfa-Abstract.html.
[Far93] S. J. Farlow. Partial differential equations for scientists and engineers. Courier Corporation, 1993.
[Góm+18] R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, et al. "Automatic chemical design using a data-driven continuous representation of molecules". In: ACS central science 4.2 (2018), pp. 268-276.
[Gur82] M. E. Gurtin. An introduction to continuum mechanics. Academic press, 1982.
[HJA20] J. Ho, A. Jain, and P. Abbeel. "Denoising Diffusion Probabilistic Models". In: Advances in Neural Information Processing Systems. 2020. URL: https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html.
[HKT22] P. Holl, V. Koltun, and N. Thuerey. "Scale-invariant Learning by Physics Inversion". In: Advances in Neural Information Processing Systems 35 (2022), pp. 5390-5403.
[HLC21] C. Huang, J. H. Lim, and A. C. Courville. “A Variational Perspective on Diffusion-Based Generative Models and Score Matching”. In: Advances in Neural Information Processing Systems. 2021, pp. 22863–22876. URL: https://proceedings.neurips.cc/paper/2021/bitstream/c11abfd29e4d9b4d4b566b01114d8486-Abstract.html.