digital_twinning

A comprehensive Python package for digital twin model updating and predictive modeling using machine learning and uncertainty quantification techniques. Overview The Digital Twinning package provides tools for creating data-driven predictive models and updating them with measurement data using Bayesian inference (MCMC). It combines surrogate modeling, machine learning, and probabilistic model updating for structural health monitoring and digital twin applications. Installation pip install digital-twinning Features Predictive Models Deep Neural Networks (DNN): Flexible neural network architectures with customizable layers and activation functions Gradient Boosted Trees (GBT): Support for multiple implementations (XGBoost, CatBoost, LightGBM, scikit-learn) Linear Regression: Basic linear regression models for baseline comparisons gPCE Models: Generalized Polynomial Chaos Expansion for uncertainty quantification Model Updating Bayesian Model Updating: MCMC-based parameter estimation using emcee Multi-Building Updates: Joint parameter estimation across multiple structures Prior and Posterior Analysis: Tools for analyzing parameter distributions Model Interpretability SHAP Analysis: Feature importance and explanation using SHAP values Sobol Sensitivity Analysis: Global sensitivity analysis for parameter importance Visualization Tools: Comprehensive plotting utilities for model analysis Key Classes PredictiveModel The base class for all predictive models. Supports training, prediction, cross-validation, and model interpretability. Methods: train(): Train the model with optional k-fold cross-validation predict(): Make predictions on new data get_shap_values(): Compute SHAP values for feature importance get_sobol_sensitivity(): Perform Sobol sensitivity analysis save_model() / load_model(): Serialize and deserialize models DigitalTwin MCMC-based Bayesian model updating for parameter estimation. Methods: update(): Update parameters using measurement data get_mean_and_var_of_posterior(): Get posterior statistics get_MAP(): Get maximum a posteriori estimate loglikelihood(): Compute log-likelihood of measurements logprior(): Compute log-prior of parameters JointManager Manage joint model updating for multiple buildings with shared parameters. Methods: update(): Perform joint update across all buildings get_joint_paramset_and_indices(): Create joint parameter space generate_joint_stdrn_simparamset(): Generate joint simulation parameter sets DNNModel Deep Neural Network implementation with PyTorch backend. Features: Flexible architecture with customizable layers Multiple activation functions (ReLU, GELU, Tanh, etc.) Dropout regularization Early stopping GPU support GBTModel Gradient Boosted Decision Trees with multiple backend options. Supported Backends: XGBoost CatBoost LightGBM scikit-learn GradientBoostingRegressor Authors and acknowledgment The code is developed by András Urbanics, Áron Friedman, Bence Popovics, Emese Vastag, Elmar Zander and Noémi Friedman in the TRACE-Structures group. This work has been funded by the European Commission Horizon Europe Innovation Action project 101092052 BUILDCHAIN License This project is licensed under the GNU General Public License v3.0 (GPL-3.0-only). See the LICENSE file for details. Related Projects gPCE_model: Generalized Polynomial Chaos Expansion uncertain_variables: Probabilistic variable management Support For issues, questions, or contributions, please refer to the project repository or contact the authors.