Learning Physical Laws/Automatic Knowledge Discovery

For the most time of the history of humankind, scientists had to discover physical laws on their own using observations. However, the increase of available data and computing power and the general advance of artificial intelligence recently triggered the idea to automate this search for physical laws. Physical law learning aims to learn the function governing a (physical) phenomenon exactly, such that they are interpretable and offer more insight than black box prediction solutions. Possible thesis topics range from applying existing algorithms to new fields or devising new algorithms to tackling theoretical questions about PDE learning.

Research at our chair

In Progress

General references

Review paper: https://arxiv.org/abs/2211.10873
Seminal work in phyiscal law learning using genetic programming: https://www.science.org/doi/abs/10.1126/science.1165893
Introducing SINDy (a form of sparse regression) to compute physical laws: https://www.pnas.org/doi/10.1073/pnas.1517384113
Modelling the physical law as a neural network: http://proceedings.mlr.press/v80/sahoo18a.html
Using transformers to compute physical laws: https://arxiv.org/abs/2204.10532
Theoretical work tackling the uniqueness problem: https://arxiv.org/abs/2210.08342

Contact

Do you have questions about our research in this area?

Please do not hesitate to contact us directly. Please send an e-mail to Jianfei Li, Postdoc in the field of Learning Physical Laws/Automatic Knowledge Discovery.

Inquiries from students are very welcome!