📢 Physics + GPs + inverse problems using #ProbabilisticNumerics 📢
At #ICML2022 we show that probabilistic ODE solvers are not just fast, but also useful for solving inverse problems! Joint work with Filip Tronarp and @PhilippHennig5. More below 🧵
At #ICML2022 we show that probabilistic ODE solvers are not just fast, but also useful for solving inverse problems! Joint work with Filip Tronarp and @PhilippHennig5. More below 🧵
The gist is: When doing inference with traditional ODE solvers we ignore their numerical error. But by being "probabilistic about the numerics", we can fit _both the ODE and the data jointly_! Which e.g. allows us to better learn parameters of oscillatory systems:
Paper: proceedings.mlr.press/v162/tronarp22a
Experiments: github.com/nathanaelbosch…
Code in #julialang: github.com/nathanaelbosch…
And if you got curious about probabilistic ODE solvers, there's more this ICML:
Experiments: github.com/nathanaelbosch…
Code in #julialang: github.com/nathanaelbosch…
And if you got curious about probabilistic ODE solvers, there's more this ICML:
https://twitter.com/pnkraemer/status/1547865620422672385
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