Theo Brown
@theo-brown.bsky.social
PhD student UCL / UKAEA | Probabilistic ML | Safe control | Nuclear fusion
🚀FreeGSNKE is now open-source! Big milestone, esp for those interested in ML/RL for fusion🚀
I've talked to many people who have been inspired by Deepmind's 2022 Nature paper on tokamak magnetic control, but struggled to get involved in ML-control for fusion problems...
🧵1/3
I've talked to many people who have been inspired by Deepmind's 2022 Nature paper on tokamak magnetic control, but struggled to get involved in ML-control for fusion problems...
🧵1/3
January 16, 2025 at 11:46 AM
🚀FreeGSNKE is now open-source! Big milestone, esp for those interested in ML/RL for fusion🚀
I've talked to many people who have been inspired by Deepmind's 2022 Nature paper on tokamak magnetic control, but struggled to get involved in ML-control for fusion problems...
🧵1/3
I've talked to many people who have been inspired by Deepmind's 2022 Nature paper on tokamak magnetic control, but struggled to get involved in ML-control for fusion problems...
🧵1/3
📣 If you've got an objective that exhibits symmetries, you should be using invariant kernel BO 📣
🚀 More sample efficient than constrained/naive BO!
🚀 More compute efficient than data augmentation!
🧵 1/4
#NeurIPS2024 #BayesianOptimisation #ai
🚀 More sample efficient than constrained/naive BO!
🚀 More compute efficient than data augmentation!
🧵 1/4
#NeurIPS2024 #BayesianOptimisation #ai
November 21, 2024 at 12:14 PM
📣 If you've got an objective that exhibits symmetries, you should be using invariant kernel BO 📣
🚀 More sample efficient than constrained/naive BO!
🚀 More compute efficient than data augmentation!
🧵 1/4
#NeurIPS2024 #BayesianOptimisation #ai
🚀 More sample efficient than constrained/naive BO!
🚀 More compute efficient than data augmentation!
🧵 1/4
#NeurIPS2024 #BayesianOptimisation #ai