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This is a really interesting idea, using machine learning to analyze dynamical systems. I've had this idea kicking around in my head about using machine learnin

This is a really interesting idea, using machine learning to analyze dynamical systems. I've had this idea kicking around in my head about using machine learning on complex time series data for a sort of anomaly detection. You could use example time series data to train a neural network to find conserved quantities in the system, essentially trying to learn the internal energy of the system, and then changes in that internal energy would be your anomalies. It looks like this paper is getting at the same basic idea, though obviously actually fleshed out and with better results than I would have thought were possible. It's going not for anomaly detection but actual forecasting, and directly isolating the non-conserved force. I'll need to start reading the related papers, I wonder if people have tried doing this with nonlinear equations, or systems where the dynamics aren't well defined? I imagine actually forecasting a nonlinear system wouldn't be possible beyond short time frames, but maybe you could still learn the underlying equation and dynamics? It would be interesting to see if you could give a neural network a bunch of nonlinear data and have it find equilibrium points, identify regions where the dynamics are almost linear, etc.

dgr582systems 6y ago
People are working on it (disclosure, that includes me - https://arxiv.org/abs/2005.13028 https://arxiv.org/abs/2005.13028) There is a special interest group at The Alan Turing Institute in the UK dedicated to working on problems of this sort. There are a number of challenges that vary depending on both the system and the forcing.
theonewolf 6y ago
Not sure if this is close to the problems you want to study, but NASA applied Deep learning to anomaly detection on spacecraft telemetry and has some early results here: https://arxiv.org/pdf/1802.04431.pdf https://arxiv.org/pdf/1802.04431.pdf I'm very interested in anomaly detection problems, so if you find anything good out there do comment back in!
scottlocklin 6y ago
It's actually not an interesting or original idea, and this is a shit tier paper; it's generally a way for a physicist to attempt to get a job when done grad school. Echo state networks do insanely well on the Mackey Glass equation and ESNs are randomly connected and use linear regression on the output node.
- marmaduke 6y ago
  > this is a shit tier paper I agree with the content if not the style of your comment, and it may do better to say why the results are trivial
  - tonisunset 6y ago
    Arxiv is not peer-reviewed and usually used for preprint versions before submitting a paper to a journal. Also it allows to make the information accessible for free to anybody.
seg_lol 6y ago
This system might be able to determine causual relationships as well as expended energy vs delivered energy and what components are driving vs being driven. >90% of all energy spent on compute will be on matrix multiplications.

dgr582systems 6y ago

People are working on it (disclosure, that includes me - https://arxiv.org/abs/2005.13028 https://arxiv.org/abs/2005.13028) There is a special interest group at The Alan Turing Institute in the UK dedicated to working on problems of this sort. There are a number of challenges that vary depending on both the system and the forcing.

theonewolf 6y ago

Not sure if this is close to the problems you want to study, but NASA applied Deep learning to anomaly detection on spacecraft telemetry and has some early results here: https://arxiv.org/pdf/1802.04431.pdf https://arxiv.org/pdf/1802.04431.pdf I'm very interested in anomaly detection problems, so if you find anything good out there do comment back in!

scottlocklin 6y ago

It's actually not an interesting or original idea, and this is a shit tier paper; it's generally a way for a physicist to attempt to get a job when done grad school. Echo state networks do insanely well on the Mackey Glass equation and ESNs are randomly connected and use linear regression on the output node.

marmaduke 6y ago
> this is a shit tier paper I agree with the content if not the style of your comment, and it may do better to say why the results are trivial
- tonisunset 6y ago
  Arxiv is not peer-reviewed and usually used for preprint versions before submitting a paper to a journal. Also it allows to make the information accessible for free to anybody.

seg_lol 6y ago

This system might be able to determine causual relationships as well as expended energy vs delivered energy and what components are driving vs being driven. >90% of all energy spent on compute will be on matrix multiplications.