61 Open source projects that are alternatives of or similar to OpenCossan

UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.

tools for scalable and non-intrusive parameter estimation, uncertainty analysis and sensitivity analysis

An example project that predicts risk of credit card default using a Logistic Regression classifier and a 30,000 sample dataset.

A powerful flow control component enabling reliability, resilience and monitoring for microservices. (面向云原生微服务的高可用流控防护组件)

Lattice gas Monte Carlo simulation code

Chaos and resiliency testing tool for Kubernetes and OpenShift

Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia

A python based, MPI enabled, Monte-Carlo calculation of 2D system using Metropolis algorithm.

A continuous-time hybridization-expansion Monte Carlo code for calculating n-particle Green's functions of the Anderson impurity model and within dynamical mean-field theory.

High-performance framework for uncertainty quantification, optimization and reinforcement learning.

a general library for fatigue and reliability

Process global-scale satellite and airborne elevation data into time series of glacier mass change: Hugonnet et al. (2021).

Main repository of the Vigor NF verification project.

Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.

HOOMD-blue example scripts.

Uncertainty-Wizard is a plugin on top of tensorflow.keras, allowing to easily and efficiently create uncertainty-aware deep neural networks. Also useful if you want to train multiple small models in parallel.

I have been deeply interested in algorithmic trading and systematic trading algorithms. This Repository contains the code of what I have learnt on the way. It starts form some basic simple statistics and will lead up to complex machine learning algorithms.

A curated list of Site Reliability and Production Engineering resources.

GOMC - GPU Optimized Monte Carlo is a parallel molecular simulation code designed for high-performance simulation of large systems

Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner

[ICCV 2021 Oral] Deep Evidential Action Recognition

📚 🐣 软件实践文集。主题不限，思考讨论有趣有料就好，包含如 系统的模型分析/量化分析、开源漫游者指南、软件可靠性设计实践…… 🥤

Bayesian tools for fitting molecular mechanics torsion parameters to quantum chemical data.

Monte Carlo simulation of energy-dispersive X-ray fluorescence spectrometers

Differential Algebra Computational Toolbox

Retrieving stuff from the web is unreliable. Airbud adds retries for production, and fixture support for test.

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc.

🤖 Run a Mayhem for API scan in GitHub Actions

NCrystal : a library for thermal neutron transport in crystals and other materials

Evaluate and improve the reliability, performance and efficiency of your Kubernetes applications.

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

Simple python/streamlit web app for European option pricing using Black-Scholes model, Monte Carlo simulation and Binomial model. Spot prices for the underlying are fetched from Yahoo Finance API.

Solvers/annealers for simulated quantum annealing on CPU and CUDA(NVIDIA GPU).

Cassandra is a Monte Carlo package to conduct atomistic simulations.

CRPropa is a public astrophysical simulation framework for propagating extraterrestrial ultra-high energy particles. https://crpropa.github.io/CRPropa3/

Compilation of public failure/horror stories related to Kubernetes

A probabilistic prototype for deployment of Fog applications.

Advanced Multilanguage Interface to CVODES and IDAS

Pythonic particle-based (super-droplet) warm-rain/aqueous-chemistry cloud microphysics package with box, parcel & 1D/2D prescribed-flow examples in Python, Julia and Matlab

A curated collection of publicly available resources on how technology and tech-savvy organizations around the world practice Site Reliability Engineering (SRE)

AdaptivePELE is a Python package aimed at enhancing the sampling of molecular simulations

Usage of policy gradient reinforcement learning to solve portfolio optimization problems (Tactical Asset Allocation).

Evoplex is a fast, robust and extensible platform for developing agent-based models and multi-agent systems on networks. It's available for Windows, Linux and macOS.

Easily add health checks to your go services

Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

Using neural network to build expressive hierarchical distribution; A variational method to accurately estimate posterior uncertainty; A fast and general method for Bayesian inference. (ICML 2018)

A library for uncertainty quantification based on PyTorch

A module to get the minimum usable engine(s)

Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, calibration, etc

ANTARES Visualizations

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

xk6 extension for running chaos experiments with k6 💣

U-Net for biomedical image segmentation

A Power BI template that provides easy to understand, actionable flow metrics and predictive analytics for your agile teams using Azure DevOps, Azure DevOps Server and/or TFS.

Optimize Ubuntu for usability, security, privacy and stability

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

Analysis of digital elevation models (DEMs)

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

(ICML 2022) Official PyTorch implementation of “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness”.

Estimation of calibration errors.