3 ms·

I'm a PhD student in robotics at Carnegie Mellon working on exactly this. It's extremely challenging for a few reasons: - the dataset is a mess. The experiment

by vrm 5y ago

I'm a PhD student in robotics at Carnegie Mellon working on exactly this. It's extremely challenging for a few reasons: - the dataset is a mess. The experiments that have been conducted on the tokamak that we have access to were done for very many different reasons and under many different configurations of the machine so there is not a clear method for disambiguating what dynamical changes are due to differences in the system vs underlying dynamical truths - the simulators available are very slow and not that accurate - the physics is hard enough that it's not possible to develop a controller in closed form (obviously) This implies that we need a version of reinforcement learning or model-predictive control that is substantially more robust and sample-efficient than currently exists. We're working on that but obviously it's an open research problem.

theptip 5y ago
Any papers you can link to on this? Sounds interesting.
- vrm 5y ago
  will repost my comment from the other one: Papers I would recommend from our collaboration on control of normalized plasma pressure: https://papers.nips.cc/paper/2019/hash/7876acb66640bad41f1e1 https://papers.nips.cc/paper/2019/hash/7876acb66640bad41f1e1... plasma profile transport modeling: https://iopscience.iop.org/article/10.1088/1741-4326/abe08d/ https://iopscience.iop.org/article/10.1088/1741-4326/abe08d/... hybrid dynamical modeling of gross plasma quantities: https://arxiv.org/abs/2006.12682 https://arxiv.org/abs/2006.12682 uncertainty quantification for plasma dynamics: https://arxiv.org/abs/2011.09588 https://arxiv.org/abs/2011.09588 It's still early days for this work and for us but we're looking at pushing reinforcement learning in methods and engineering to solve this problem.
pfdietz 5y ago
I'm assuming disruption mitigation is a big part of what you're looking at? Controlling a normal state plasma would be nice, but recognizing incipient disruptions is absolutely critical if ITER (or ARC) is to function at all. One unmitigated disruption could break ITER.
- vrm 5y ago
  Yeah so the paper I linked on contextual bayesian optimization (https://papers.nips.cc/paper/2019/hash/7876acb66640bad41f1e1371ef30c180-Abstract.html https://papers.nips.cc/paper/2019/hash/7876acb66640bad41f1e1...) does a combination of controlling for beta and optimizing linear MHD stability --- which is part of the problem. One of our collaborators has been working precisely on disruption prediction and mitigation but it's on most of our minds: https://www.pppl.gov/news/2021/artificial-intelligence-helps-prevent-disruptions-fusion-devices https://www.pppl.gov/news/2021/artificial-intelligence-helps....

by vrm 5y ago

theptip 5y ago

Any papers you can link to on this? Sounds interesting.

vrm 5y ago
will repost my comment from the other one: Papers I would recommend from our collaboration on control of normalized plasma pressure: https://papers.nips.cc/paper/2019/hash/7876acb66640bad41f1e1 https://papers.nips.cc/paper/2019/hash/7876acb66640bad41f1e1... plasma profile transport modeling: https://iopscience.iop.org/article/10.1088/1741-4326/abe08d/ https://iopscience.iop.org/article/10.1088/1741-4326/abe08d/... hybrid dynamical modeling of gross plasma quantities: https://arxiv.org/abs/2006.12682 https://arxiv.org/abs/2006.12682 uncertainty quantification for plasma dynamics: https://arxiv.org/abs/2011.09588 https://arxiv.org/abs/2011.09588 It's still early days for this work and for us but we're looking at pushing reinforcement learning in methods and engineering to solve this problem.

pfdietz 5y ago

I'm assuming disruption mitigation is a big part of what you're looking at? Controlling a normal state plasma would be nice, but recognizing incipient disruptions is absolutely critical if ITER (or ARC) is to function at all. One unmitigated disruption could break ITER.

vrm 5y ago
Yeah so the paper I linked on contextual bayesian optimization (https://papers.nips.cc/paper/2019/hash/7876acb66640bad41f1e1371ef30c180-Abstract.html https://papers.nips.cc/paper/2019/hash/7876acb66640bad41f1e1...) does a combination of controlling for beta and optimizing linear MHD stability --- which is part of the problem. One of our collaborators has been working precisely on disruption prediction and mitigation but it's on most of our minds: https://www.pppl.gov/news/2021/artificial-intelligence-helps-prevent-disruptions-fusion-devices https://www.pppl.gov/news/2021/artificial-intelligence-helps....