uqdeepnn 0.2.0

PyTorch uncertainty quantification toolkit with deep ensembles, Bayes-by-Backprop VI, Laplace, SGLD, MC Dropout, Gaussian Processes, and scientific ML backbones.

Deep-UQ is a PyTorch toolkit for uncertainty-aware machine learning.

It provides 20+ uncertainty quantification methods, Gaussian-process models, scientific machine learning backbones, and evaluation metrics — all behind a unified predict_uq() API returning calibrated uncertainty estimates.

Install

pip install uqdeepnn

Quick Example

from deepuq.models import MLP
from deepuq.methods import LaplaceWrapper

model = MLP(input_dim=1, hidden_dims=[64, 64], output_dim=1)
# ... train as usual ...

la = LaplaceWrapper(model, likelihood="regression", hessian_structure="kron")
la.fit(train_loader)
result = la.predict_uq(x_test)
# result.mean, result.epistemic_var, result.total_var

UQ Methods

• Post-hoc (no retraining): MC Dropout, Laplace, SWAG, Temperature Scaling, Test-Time Augmentation
• Single forward pass: SNGP, Evidential Deep Learning
• Ensembles: Deep Ensembles, Batch Ensemble, Packed Ensemble
• Bayesian: Variational Inference (BBB, Flipout, Last-Layer), SVGD
• MCMC: SGLD, SGHMC, Cyclical SGMCMC
• Gaussian Processes: Exact, Sparse, Multi-task, Deep Kernel, Multi-Fidelity
• Conformal: Split, CQR, Weighted, Adaptive
• Calibration: Temperature Scaling, Vector Scaling, Isotonic Regression
• Decision: Selective Prediction with AURC evaluation

Modules

• deepuq.methods — All UQ method wrappers
• deepuq.models — Neural architectures (MLP, FNO, DeepONet, GNO, PINN, CNN, UNet, GPs)
• deepuq.metrics — ECE, CRPS, AUROC, PICP, Brier score, interval score, AURC
• deepuq.active — Active learning (Uncertainty Sampling, BALD)
• deepuq.constraints — Physics constraints (positivity, bounds, conservation, monotonicity)
• deepuq.propagation — Uncertainty propagation for autoregressive rollouts

Scientific ML Backbones

DeepONet, FNO (2D/3D), Graph Neural Operators, CNN/ResNet/U-Net surrogates, PINNs, Diffusion models — all with first-class UQ support.

Documentation

• Docs: https://vispikarkaria.github.io/Deep-UQ/
• Tutorials: https://vispikarkaria.github.io/Deep-UQ/tutorials/
• API reference: https://vispikarkaria.github.io/Deep-UQ/api/
• GitHub: https://github.com/Vispikarkaria/Deep-UQ

Package names

• PyPI package: uqdeepnn
• Python import: deepuq