Projects taken

1. Data-Driven Computational Disease Spread Modeling: From Measurement to Paramatrization and Control, David Krantz and Federica Milinanni.

2. Stochastic modified equations and adaptive stochastic gradient algorithms, Jonas Cederberg and Martin Lindström.

3. Stochastic_control_neural_network.pdf , Harald Attling and Shervin Gohari Moghadam

4. SIR epidemic model review, Simen Thingstad and Anton Normelius

5. The invariant density of a chaotic dynamical system with small noise, Viktor Pontéus and Karl Maltering Ruderfors

6. Overcoming the curse of dimensionality:  Solving high-dimensional. partial differential equations using deep learning, Björn Wehlin and Paul Hedvall.

7. Levy-processes, Romain Pages and Adrien Delaplace.

8. Pricing under rough volatility, Simon Carlsson and Adam Vernersson.

9. A Proposal on Machine Learning via Dynamical Systems, Siobhan Correnty and Yuqi Shao.

10. Epidemi_model_engblom, Anton Bergman and Niklas Renström.

11. "A Proposal on Machine Learning via Dynamical Systems" (sde_neural_network.pdf), Johanna Frost and Tove Ågren.

12. Fractional Brownian motion: theory and applications, Isak Ågren and Teodor Elmfeldt.

13. Fractal random field" (A Fourier–Wavelet Monte Carlo Method for Fractal Random Fields),  Axel Engström and Alicia Bråtner.

14. Levy processes, Olof Hummelgren and Ziming Wang.

15.  A Mathematical Framework for Stochastic Climate Models, Julia Schauman and Jonatan Risberg.

16. SIR epidemic model 2, Rafael Lavatt and Christoffer Limér.

17.  Quasi Monte Carlo Methods, Nils Rack.

18. Reconciling modern machine-learning practice and the classical bias-variance trade-off, Anna Barysheva and Karin Levina Larsson.

19. Multilevel Monte Carlo Path Simulation, Sirak Ghebramlak.

20. Malliavin calculus in finance, Johan Hellberg and Kasper Johansson

21. On the long-time integration of stochastic gradient systems, Johan Wärnegård.