# Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers

arXiv: https://arxiv.org/abs/2312.12610 · HTML: https://mlsanity.com/paper/2312.12610/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 94 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://mlsanity.com/paper/1809.11165/) (2018)
- [Max-value Entropy Search for Efficient Bayesian Optimization](https://mlsanity.com/paper/1703.01968/) (2017)
- [Bayesian Optimization of Composite Functions](https://mlsanity.com/paper/1906.01537/) (2019)

## Built on by (most cited first, 4 of 4)

- [CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization](https://mlsanity.com/paper/2405.07760/) (2024)
- [Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models](https://mlsanity.com/paper/2507.15976/) (2025)
- [TGLF-WINN: Data-Efficient Deep Learning Surrogate for Turbulent Transport Modeling in Fusion](https://mlsanity.com/paper/2509.07024/) (2025)
- [A Shortcut to Statistically Steady-State Turbulence with Flow Matching](https://mlsanity.com/paper/2607.13022/) (2026)

## Abstract (arXiv)

This work presents the PORTALS framework, which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy. The efficiency of PORTALS is benchmarked against standard methods, and its full potential is demonstrated on a unique, simultaneous 5-channel (electron temperature, ion temperature, electron density, impurity density and angular rotation) prediction of steady-state profiles in a DIII-D ITER Similar Shape plasma with GPU-accelerated, nonlinear CGYRO. This paper also provides general guidelines for accurate performance predictions in burning plasmas and the impact of transport modeling in fusion pilot plants studies.