Position: Opportunities Exist for Machine Learning in Magnetic Fusion Energy

Lucas Spangher, Allen M. Wang, Andrew Maris, Myles Stapelberg, Viraj Mehta, Alex Saperstein, Stephen Lane-Walsh, Akshata Kishore Moharir, Alessandro Pau, Cristina Rea
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:46303-46322, 2024.

Abstract

Magnetic confinement fusion may one day provide reliable, carbon-free energy, but the field currently faces technical hurdles. In this position paper, we highlight six key research challenges in the field of fusion energy that we believe should be research priorities for the Machine Learning (ML) community because they are especially ripe for ML applications: (1) disruption prediction, (2) simulation and dynamics modeling (3) resolving partially observed data, (4) improving controls, (5) guiding experiments with optimal design, and (6) enhancing materials discovery. For each problem, we give background, review past ML work, suggest features of future models, and list challenges and idiosyncrasies facing ML development. We also discuss ongoing efforts to update the fusion data ecosystem and identify opportunities further down the line that will be enabled as fusion and its data infrastructure advance. It is our position that fusion energy offers especially exciting opportunities for ML practitioners to impact decarbonization and the future of energy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-spangher24a, title = {Position: Opportunities Exist for Machine Learning in Magnetic Fusion Energy}, author = {Spangher, Lucas and Wang, Allen M. and Maris, Andrew and Stapelberg, Myles and Mehta, Viraj and Saperstein, Alex and Lane-Walsh, Stephen and Moharir, Akshata Kishore and Pau, Alessandro and Rea, Cristina}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {46303--46322}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/spangher24a/spangher24a.pdf}, url = {https://proceedings.mlr.press/v235/spangher24a.html}, abstract = {Magnetic confinement fusion may one day provide reliable, carbon-free energy, but the field currently faces technical hurdles. In this position paper, we highlight six key research challenges in the field of fusion energy that we believe should be research priorities for the Machine Learning (ML) community because they are especially ripe for ML applications: (1) disruption prediction, (2) simulation and dynamics modeling (3) resolving partially observed data, (4) improving controls, (5) guiding experiments with optimal design, and (6) enhancing materials discovery. For each problem, we give background, review past ML work, suggest features of future models, and list challenges and idiosyncrasies facing ML development. We also discuss ongoing efforts to update the fusion data ecosystem and identify opportunities further down the line that will be enabled as fusion and its data infrastructure advance. It is our position that fusion energy offers especially exciting opportunities for ML practitioners to impact decarbonization and the future of energy.} }
Endnote
%0 Conference Paper %T Position: Opportunities Exist for Machine Learning in Magnetic Fusion Energy %A Lucas Spangher %A Allen M. Wang %A Andrew Maris %A Myles Stapelberg %A Viraj Mehta %A Alex Saperstein %A Stephen Lane-Walsh %A Akshata Kishore Moharir %A Alessandro Pau %A Cristina Rea %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-spangher24a %I PMLR %P 46303--46322 %U https://proceedings.mlr.press/v235/spangher24a.html %V 235 %X Magnetic confinement fusion may one day provide reliable, carbon-free energy, but the field currently faces technical hurdles. In this position paper, we highlight six key research challenges in the field of fusion energy that we believe should be research priorities for the Machine Learning (ML) community because they are especially ripe for ML applications: (1) disruption prediction, (2) simulation and dynamics modeling (3) resolving partially observed data, (4) improving controls, (5) guiding experiments with optimal design, and (6) enhancing materials discovery. For each problem, we give background, review past ML work, suggest features of future models, and list challenges and idiosyncrasies facing ML development. We also discuss ongoing efforts to update the fusion data ecosystem and identify opportunities further down the line that will be enabled as fusion and its data infrastructure advance. It is our position that fusion energy offers especially exciting opportunities for ML practitioners to impact decarbonization and the future of energy.
APA
Spangher, L., Wang, A.M., Maris, A., Stapelberg, M., Mehta, V., Saperstein, A., Lane-Walsh, S., Moharir, A.K., Pau, A. & Rea, C.. (2024). Position: Opportunities Exist for Machine Learning in Magnetic Fusion Energy. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:46303-46322 Available from https://proceedings.mlr.press/v235/spangher24a.html.

Related Material