Physics-Informed Neural Network SurrogaTe for Rapidly Identifying Parameters in Energy Systems
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Dec 10, 2025 - Python
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bayesian-calibration
Here are 12 public repositories matching this topic...
A toolbox for the calibration and evaluation of simulation models.
evolutionary-algorithms abc model-calibration uncertainty-quantification sensitivity-analysis bayesian-optimization active-learning optimisation uncertainty-analysis experimental-design sbi approximate-bayesian-computation reliability-analysis surrogate-models bayesian-quadrature surrogate-based-optimization optimal-experimental-design bayesian-calibration history-matching simulation-based-inference
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Jun 1, 2026 - Python
A python package for surrogate models that interface with calibration and other tools
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Jul 14, 2026 - Jupyter Notebook
Conduct parametric analysis on EnergyPlus models in R
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Nov 26, 2025 - R
Morris global sensitivity analysis, Bayesian DREAMzs calibration, and multi-objective optimization of green infrastructure using the RHESSys ecohydrological model.
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Jun 17, 2024 - R
Data and code for Jia and Chong (2020): Hongyuan Jia and Adrian Chong (2020). eplusr: A framework for integrating building energy simulation and data-driven analytics. (Accepted in Energy and Buildings).
r data-analysis multi-objective-optimization data-exploration energyplus parametric-analysis eplusr bayesian-calibration data-driven-analytics
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Dec 14, 2020
Workshop for calisim: A toolbox for the calibration and evaluation of simulation models.
evolutionary-algorithms abc bayesian-inference uncertainty-quantification sensitivity-analysis bayesian-statistics active-learning sbi approximate-bayesian-computation surrogate-models surrogate-based-optimization optimal-experimental-design bayesian-calibration history-matching
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Mar 31, 2026 - Jupyter Notebook
Bayesian adaptive calibration and optimal design (BACON) paper accepted at NeurIPS 2024
pytorch bayesian-inference gaussian-processes variational-inference active-learning normalizing-flows bayesian-calibration conditional-normalizing-flows
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Feb 25, 2025 - Jupyter Notebook
DOEoptimizer is a package that implements four optimization algorithms specifically designed for optimizing design of physical experiments criteria (a matrix input function).
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Apr 14, 2025 - R
Marine-terminating glacier workflow coupling modified OGGM, PyGEM, and SERMeQ with a new calving law and AdaPBS Bayesian joint calibration; includes configs, dry-run quickstart, tests, visuals, and reproducibility links.
python scientific-computing reproducibility glacier cryosphere oggm glacier-modelling bayesian-calibration frontal-ablation marine-terminating pygem-glacier sermeq adaptive-particle-batch-smoother
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Jul 9, 2026 - Python
Metodologie generování a validace dat pro modely umělé inteligence ve stavebním inženýrství
markov-chain-monte-carlo simulation-modeling monte-carlo-methods synthetic-data stochastic-modeling bayesian-calibration markov-processes robotic-masonry-system posterior-predictive-simulation training-data-for-artificial-intelligence
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Mar 24, 2026 - HTML
Design of physical experiments for expensive computer code calibration
simulated-annealing gaussian-process bayesian-calibration design-of-physical-experiments optimality-criteria bayesian-optimality-criteria
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Jun 5, 2025 - R
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