UQPyL

Uncertainty Quantification, Calibration and Optimization in Python

  • Define reusable problems for uncertainty quantification, calibration, and optimization workflows
  • Run design of experiments, sensitivity analysis, and Bayesian inference methods
  • Build surrogate models for expensive simulations and surrogate-assisted optimization
  • Use benchmark problems to test and compare algorithms

Problem and Workflow

UQPyL starts from a reusable problem definition, then connects sampling, analysis, inference, surrogate modelling, calibration, and optimization around the same interface.

Problem First
  1. 01

    Define a Problem

    Wrap variables, bounds, objectives, constraints, or simulations into one shared entry point.

    Problem/ModelProblem
  2. 02

    Run a Method

    Pass the same problem into DOE, analysis, optimization, inference, calibration, or surrogate modules.

    DOE/Analysis/Optimization/Inference/Calibration/Surrogate
  3. 03

    Inspect Results

    Read standard result objects or inspect saved runtime records.

    verbose/log/save
Module MapUQPyL architecture map

Six Functional Modules

Each module covers one part of the uncertainty quantification, calibration, and optimization workflow.

Design of Experiment

01

Generate samples for model exploration, sensitivity analysis, inference, and surrogate model training.

Design of Experiment
LHSSobolSaltelli
View

Surrogate Model

02

Approximate expensive models and accelerate analysis or optimization workflows.

Surrogate Model
KrigingGPRBF
View

Sensitivity Analysis

03

Quantify how input uncertainties influence outputs and identify important variables.

Sensitivity Analysis
SobolFASTMorris
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Inference

04

Estimate uncertain parameters and posterior distributions with Bayesian sampling methods.

Inference
MHDEMCDREAM-ZS
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Calibration

05

Calibrate model parameters and support hydrological or external-model workflows.

Calibration
GLUESUFI2IES
View

Optimization

06

Search for optimal decisions with single-objective, multi-objective, and surrogate-assisted algorithms.

Optimization
SCE-UANSGA-IIASMO
View

Application Cases

Representative studies across hydrological calibration, surrogate-assisted optimization, and connected uncertainty workflows.

Dongjiang River Basin

Hydrological model calibration with sensitivity analysis and uncertainty evaluation.

Dongjiang River Basin

Surrogate-Assisted Optimization

Optimization under expensive simulation settings with adaptive surrogate modelling.

Surrogate-Assisted Optimization

Workflow Overview

A compact end-to-end view of how problems, modules, and workflows connect in practice.

Workflow Overview

Start Here

Use the homepage as the main documentation gateway.