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| Property | Value |
|---|---|
| Type | Surrogate configuration (no weights training required) |
| Framework | scikit-learn + numpy/scipy GP |
| Default Surrogate | Gaussian Process (Matérn-5/2, ARD) |
| Acquisition | Expected Improvement, qParEGO, Constrained EI |
| Model | Level | Description |
|---|---|---|
| Polynomial RSM | 2 | Quadratic response surface |
| Gaussian Process | 3 | Matérn kernel with ARD |
| Random Forest | 3 | 100-tree ensemble |
| Gradient Boosting | 3 | Stage-wise additive model |
| Neural Network | 3 | 2-hidden-layer MLP |
| Bayesian Neural Network | 4 | MC dropout uncertainty |
surrogate_config.json — Full surrogate and acquisition configurationconfig.json — Model metadata1from huggingface_hub import hf_hub_download
2import json
3
4path = hf_hub_download("alirezaaminzadeh/scalelab-surrogate-config", "surrogate_config.json")
5with open(path) as f:
6 config = json.load(f)
7print(config["default_surrogate"])