RAVEN is a flexible and multi-purpose probabilistic risk analysis, validation and uncertainty quantification, parameter optimization, model reduction and data knowledge-discovering framework.

A Library for Uncertainty Quantification.

a modeling environment tailored to parameter estimation in dynamical systems

A phenology modelling framework in R

[ICCV 2021 Oral] Deep Evidential Action Recognition

Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.

Delta hedging under SABR model

System Dynamics Review (2021)

Parameter estimation and model calibration using Genetic Algorithm optimization in Python.

[CVPR 2023] Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection

Calibration of the monodomain model coupled with the Rogers-McCulloch model for the ionic current: design of a protocol for impulse delivery from an ATP device.

A collection of time-efficient state estimation algorithms for the medium-fidelity WindFarmSimulator (WFSim) control model

Simulating and Optimising Dynamical Models in Python 3

An overview about PROFOUND code, data, protocols and algorithms for interfacing, calibrating and comparing forest models

ARBO is a Matlab/C++ package for simulation and analysis of arbovirus nonlinear dynamics.

An efficient Java™ solver implementation for SBML

Official code for "On Calibrating Diffusion Probabilistic Models"

pycalibrate is a Python library to visually analyze model calibration in Jupyter Notebooks

Codebase for "A Consistent and Differentiable Lp Canonical Calibration Error Estimator", published at NeurIPS 2022.

Calibration of a wind erosion model using remote sensing-derived vegetation characteristics