morgenfryd
The aim of morgenfryd is to uncover the mathematical laws governing our world by automating the generation of explicit, verifiable, high-fidelity models of dynamical systems
Dynamical systems are modelled using explicitly defined equational systems generated manually by domain experts.
✅ The governing equations are well-known, verifiable and explainable. It is easy to extend systems and learnings transfer from one domain to another.
❌ Very time-consuming, cost-intensive and manual.
Taking advantage of large scale machine learning, models are now trained to replicate observed data. The governing equations are then unknown to us, they are instead implicitly learned - typically using diffussion models, neural ODEs or other deep learning approaches.
✅ Brute-force, automated and hugely scalable. Can be applied to a variety of systems without having to understand the underlying dynamics.
❌ Black box. No understanding of the governing dynamics and poor ability to extrapolate beyond training data. Little new knowledge of the fundamentals of the governing equations of the world are acquired and what is learned is not transferable between domains.
Using recent advances in reasoning capabilities we can combine the two above approaches to instead automate the generation of explicit governing equations. This allows us to get the best of both worlds.
✅ Automation, scale, explainability, verifiabilty, transfer of knowledge between domains and a chance to understand the governing equations of our world.
Some of the most important fields that rely heavily on modelling of dynamicals systems and for which an improvement in modelling capabilities will have a large impact: