Reinforcement Learning
stable-baselines3
Joblib
PyTorch
tabular-regression
xgboost
femtosecond-laser
hydrogel
GelMA
HAMA
laser-machining
SAC
materials-science
manufacturing
ml-intern
Instructions to use TWLab/femtosecond-laser-hydrogel-etching-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use TWLab/femtosecond-laser-hydrogel-etching-model with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="TWLab/femtosecond-laser-hydrogel-etching-model", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
| {"features": ["power_mW", "repetition_rate_kHz", "scan_speed_mm_s", "pulse_duration_fs", "wavelength_nm", "num_passes", "spot_diameter_um", "focal_offset_um", "water_content", "threshold_fluence_J_cm2", "absorption_depth_nm", "incubation_coefficient", "refractive_index", "youngs_modulus_kPa", "degree_of_methacrylation", "crosslink_density_mol_m3", "two_photon_cross_section_GM", "pulse_energy_uJ", "peak_fluence_J_cm2", "effective_pulses", "overlap_percent"], "targets": ["etch_depth_um", "etch_width_um", "surface_roughness_Sa_um", "aspect_ratio", "side_wall_angle_deg"]} |