| Approximate Bayesian neural operators: Uncertainty quantification for parametric PDEs E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig arXiv preprint arXiv:2208.01565, 2022 | 20 | 2022 |
| Bayesian filtering for ODEs with bounded derivatives E Magnani, H Kersting, M Schober, P Hennig arXiv preprint arXiv:1709.08471, 2017 | 10 | 2017 |
| Uncertainty quantification for fourier neural operators T Weber, E Magnani, M Pförtner, P Hennig ICLR 2024 workshop on AI4DifferentialEquations in science, 2024 | 6 | 2024 |
| Full history recursive multilevel Picard approximations for ordinary differential equations with expectations C Beck, M Hutzenthaler, A Jentzen, E Magnani arXiv preprint arXiv:2103.02350, 2021 | 6 | 2021 |
| Linearization turns neural operators into function-valued Gaussian processes E Magnani, M Pförtner, T Weber, P Hennig arXiv preprint arXiv:2406.05072, 2024 | 5 | 2024 |
| Learning convolution operators on compact Abelian groups E Magnani, E De Vito, P Hennig, L Rosasco arXiv preprint arXiv:2501.05279, 2025 | | 2025 |
