Machine learning models, code, sample data for approximating the computations of the Genray/CQL3D codes.

Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. The machine learning models use a database of 16,000+ GENRAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods ensure that the database covers the range of 9 input parameters ($n_{e0}$, $T_{e0}$, $I_p$, $B_t$, $R_0$, $n_{||}$, $Z_{eff}$, $V_{loop}$, $P_{LHCD}$) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to ~ms with high accuracy across the input parameter space.

• Anaconda, GPflow, Jupyter notebook, ONNX, ONNX Runtime, PyTorch.
• Mac OSX, linux and Windows.

• install dependencies

• Load ONNX model

onnx_model = onnx.load("MLP_trained_power.onnx")
onnx.checker.check_model(onnx_model)

Contributors names and contact info

• 1.0
  • Initial Release

This project is licensed under the BSD License - see the LICENSE.md file for details.

• data source