Surrogate modelling

Multi fidelity models Data-driven multi-scale sampling, multi-resolution, super-resolution 2020-08-24 – 2023-11-20

Machine learning for partial differential equations 2017-05-15 – 2023-11-17

Physics-informed neural networks 2019-10-15 – 2023-10-19

Neural PDE operator learning Especially forward operators. Image-to-image regression, where the images encode a physical process. 2019-10-15 – 2023-10-19

Probabilistic numerics 2023-07-13 – 2023-09-25

Machine learning for climate systems 2020-04-02 – 2023-09-25

Materials informatics Machine learning in condensed matter physics, chemistry and materials science 2023-08-01 – 2023-08-08

Learnable coarse-graining Approximate meso-scale physics 2020-08-24 – 2023-08-01

Bayesian model calibration 2017-04-11 – 2023-06-01

Neural learning dynamical systems 2018-08-13 – 2023-05-23

Differentiable PDE solvers 2017-05-15 – 2023-05-15

Conformal prediction 2016-12-26 – 2023-05-05

Model order reduction 2015-03-22 – 2023-04-21

ΦFlow A modern python computational fluid dynamics library for ML research 2022-06-24 – 2023-04-17

Machine learning for physical sciences Turbulent mixing at the boundary between disciplines with differing inertia and viscosity 2017-05-15 – 2022-12-07

Feedback system identification, linear 2016-07-27 – 2022-01-21

Sequential experiments Especially multiple sequential experiments 2021-08-04

Uncertainty quantification 2016-12-26 – 2021-07-06

Adaptive design of experiments Minesweeper++ 2017-04-11 – 2020-10-13

Emulators and surrogate models via ML Shortcuts in scientific simulation using ML 2020-08-12 – 2020-08-26

Coarse graining 2014-11-11 – 2015-12-02