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CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.
sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has you covered.
[ICCV-2025] TurboReg: TurboClique for Robust and Efficient Point Cloud Registration
👁 pyPESTO
python Parameter EStimation TOolbox
Probabilistic Inference on Noisy Time Series
A system for scientific simulation-based inference at scale.
tools for scalable and non-intrusive parameter estimation, uncertainty analysis and sensitivity analysis
Workflow engine for exploration of simulation models using high throughput computing
👁 ControlSystemIdentification.jl
System Identification toolbox, compatible with ControlSystems.jl
👁 LowLevelParticleFilters.jl
State estimation, smoothing and parameter estimation using Kalman and particle filters.
👁 AMICI
High-performance sensitivity analysis for large ordinary differential equation models
Introduction to statistics featuring Python. This series of lecture notes aim to walk you through all basic concepts of statistics, such as descriptive statistics, parameter estimations, hypothesis testing, ANOVA and etc. All codes are straightforward to understand.
Fast and automatic structural identifiability software for ODE systems
Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)
A library for using direct collocation in the optimization of dynamic systems.
MADS: Model Analysis & Decision Support
Framework for dynamical system identification of floating-base rigid body tree structures
a modeling environment tailored to parameter estimation in dynamical systems
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
👁 PEtab
PEtab - an SBML and TSV based data format for parameter estimation problems in systems biology
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