Presentations

Code used to create some presentations using manim and manim-slides python packages.

---

• [2024-10-15] Non-linear inverse problems and applications to image reconstruction: the resources for the talk given in the Applied mathematics seminar at Laboratoire de Mathématiques Jean Leray (LMJL) can be found in:
  • Slides | pdf | code |

Based on papers:

1. Nonlinear approximation spaces for inverse problems | arXiv | Analysis and Applications Vol. 21, No. 01, pp. 217-253 (2023) | code |
2. High order recovery of geometric interfaces from cell-average data | arXiv | Accepted for publication in ESAIM: Mathematical Modelling and Numerical Analysis | code |

---

• [2024-05-22] High order recovery of geometric interfaces from cell-average data: The resources for the talk given in the Lions-Magenes days 2024 can be found in:
  • Slides | pdf | code |

Based on papers:

1. Nonlinear approximation spaces for inverse problems | arXiv | Analysis and Applications Vol. 21, No. 01, pp. 217-253 (2023) | code |
2. High order recovery of geometric interfaces from cell-average data | arXiv | Accepted for publication in ESAIM: Mathematical Modelling and Numerical Analysis | code |

---

• [2024-05-06] Ph.D. thesis slides can be found in:
  • Slides | pdf | code |
  • Manuscript

Based on papers:

1. Reduced Order Modeling for Elliptic Problems with High Contrast Diffusion Coefficients: | HAL | ESAIM: Mathematical Modelling and Numerical Analysis 57, no. 5, pp. 2775–2802 (2023) | code |
2. Nonlinear approximation spaces for inverse problems | arXiv | Analysis and Applications Vol. 21, No. 01, pp. 217-253 (2023) | code |
3. High order recovery of geometric interfaces from cell-average data | arXiv | Accepted for publication in ESAIM: Mathematical Modelling and Numerical Analysis | code |
4. Nonlinear Compressive Reduced Basis Approximation for PDE’s | HAL | Comptes Rendus. Mécanique 351, no. S1, 1–18 (2023) | code |
5. Deep Learning-based Schemes for Singularly Perturbed Convection-Diffusion Problems | arXiv | ESAIM: Proceedings and Surveys, Vol. 73, pp. 48-67 (2023) | code |
6. State Estimation of Urban Air Pollution with Statistical, Physical, and Super-Learning Graph Models | arXiv | Advances in Computational Science and Engineering 2, no. 2, 130–51 (2024) | code |

---

To start the slide show:

• pnpm install
• pnpm dev
• visit http://localhost:3030

Edit the slides.md to see the changes.

Learn more about Slidev at the documentation.

Create a new project:

pnpm create slidev

Then add figures, slides, etc. To open each time use:

pnpm slidev --open

Finally export using the browser IDE (recommended) or via:

pnpm slidev export --with-clicks