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'.

System Dynamics Review (2021)

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

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

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.

Optimal delta hedging with SABR model

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

ARBO is a package for simulation and analysis of arbovirus nonlinear dynamics.

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

An efficient Java™ solver implementation for SBML

Official code for "On Calibrating Diffusion Probabilistic Models"

Simulating and Optimising Dynamical Models in Python 3

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.

We address the calibration of SEIR-like epidemiological models from daily reports of COVID-19 infections in New York City, during the period 01-Mar-2020 to 22-Aug-2020. Our models account for different types of disease severity, age range, sex and spatial distribution. The manuscript related to such simulations can be found in https://arxiv.org/…