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abaqus-fdnn-feitao

abc

Approximate Bayesian Computation - Sequential Monte Carlo implementation

abc-toy-problems

A series of 4 toy problems to demonstrate the utility and function of likelihood-free Bayesian inference, Approximate Bayesian Computation (ABC)

abc-vae

The code for ABC-VAE

abcpmc

Approximate Bayesian Computation Population Monte Carlo

active-subspace_paul-g.-constantine

adcme.jl

Automatic Differentiation Library for Computational and Mathematical Engineering

aet

Auto-Encoding Transformations (AETv1), CVPR 2019

algos-for-inverse-problems

annotated_deep_learning_paper_implementations

🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

as-data-sets

Active Subspace Data Sets

astroabc

Approximate Bayesian Computation Sequential Monte Carlo sampler for parameter estimation.

athena

ATHENA: Advanced Techniques for High dimensional parameter spaces to Enhance Numerical Analysis

attentional-autoencoders-for-hyperspectral-dimensionality-reduction

This repository contains the data and code for the paper, "Attention Based Convolution Autoencoder for Dimensionality Reduction in Hyperspectral Images".

awesome-cae

A curated list of awesome CAE frameworks, libraries and software.

awesome-normalizing-flows

A list of awesome resources on normalizing flows.

bayesfast

Next generation Bayesian analysis tools for efficient posterior sampling and evidence estimation.

benchmarkfcns

A collection of mathematical test functions for benchmarking optimization algorithms.

bgolearn

A Bayesian global optimization package for material design

book2_beauty-of-data-visualization

Book_2_《可视之美》 | 鸢尾花书：从加减乘除到机器学习；本册草稿正在改版，正在上传

bsdr

Bayesian Supervised Dimensionality Reduction

cec2005

cec2014

chaotic-gsa-for-engineering-design-problems

All nature-inspired algorithms involve two processes namely exploration and exploitation. For getting optimal performance, there should be a proper balance between these processes. Further, the majority of the optimization algorithms suffer from local minima entrapment problem and slow convergence speed. To alleviate these problems, researchers are now using chaotic maps. The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's gravity principle and laws of motion. It uses 10 chaotic maps for global search and fast convergence speed. Basically, in GSA gravitational constant (G) is utilized for adaptive learning of the agents. For increasing the learning speed of the agents, chaotic maps are added to gravitational constant. The practical applicability of CGSA has been accessed through by applying it to nine Mechanical and Civil engineering design problems which include Welded Beam Design (WBD), Compression Spring Design (CSD), Pressure Vessel Design (PVD), Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss (TBT), Stepped Cantilever Beam design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with seven state of the art stochastic algorithms particularly Constriction Coefficient based Particle Swarm Optimization and Gravitational Search Algorithm (CPSOGSA), Standard Gravitational Search Algorithm (GSA), Classical Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Continuous Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The experimental results indicate that CGSA shows efficient performance as compared to other seven participating algorithms.

chatgpta-prompt-engineering-guide

:octopus: Guides, papers, lecture, and resources for prompt engineering

cinnamon

Invertible neural network for gravitational wave parameter estimation

cnn-surrogate

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

co-blade

Software for Analysis and Design of Composite Rotors

compared_rvm_svr_lssvr

An sklearn style implementation of the Relevance Vector Machine (RVM).

composite_laminate_monte_carlo

A monte carlo simulation for the performance of composite laminate plates with stochastic material properties.