Pinned Repositories
Astro.IQ
Machine Learning + Spacecraft Trajectory Optimisation
bae_tuchuang
picstorage for baidu app engine
Catch2
A modern, C++-native, header-only, test framework for unit-tests, TDD and BDD - using C++11, C++14, C++17 and later (or C++03 on the Catch1.x branch)
dde_solver
Shampine and Thompson's DDE_SOLVER, a Fortran library for delay differential equations.
dde_solver-1
C/C++ bindings for Shampine and Thompson's DDE_SOLVER, a Fortran library for delay differential equations.
ddeabm
Modern Fortran implementation of the DDEABM Adams-Bashforth algorithm
decimal_for_cpp
Decimal data type for C++
DEN-ARMOEA
# Introduction of DNN-AR-MOEA This repository contains code necessary to reproduce the experiments presented in Evolutionary Optimization of High-DimensionalMulti- and Many-Objective Expensive ProblemsAssisted by a Dropout Neural Network. Gaussian processes are widely used in surrogate-assisted evolutionary optimization of expensive problems. We propose a computationally efficient dropout neural network (EDN) to replace the Gaussian process and a new model management strategy to achieve a good balance between convergence and diversity for assisting evolutionary algorithms to solve high-dimensional multi- and many-objective expensive optimization problems. mainlydue to the ability to provide a confidence level of their outputs,making it possible to adopt principled surrogate managementmethods such as the acquisition function used in Bayesian opti-mization. Unfortunately, Gaussian processes become less practi-cal for high-dimensional multi- and many-objective optimizationas their computational complexity is cubic in the number oftraining samples. # References If you found DNN-AR-MOEA useful, we would be grateful if you cite the following reference: Evolutionary Optimization of High-DimensionalMulti- and Many-Objective Expensive ProblemsAssisted by a Dropout Neural Network (IEEE Transactions on Systems, Man and Cybernetics: Systems).
dop853
Modern Fortran Edition of Hairer's DOP853 ODE Solver
drake
Model-based design and verification for robotics.
leaz's Repositories
leaz/Astro.IQ
Machine Learning + Spacecraft Trajectory Optimisation
leaz/Catch2
A modern, C++-native, header-only, test framework for unit-tests, TDD and BDD - using C++11, C++14, C++17 and later (or C++03 on the Catch1.x branch)
leaz/dde_solver
Shampine and Thompson's DDE_SOLVER, a Fortran library for delay differential equations.
leaz/dde_solver-1
C/C++ bindings for Shampine and Thompson's DDE_SOLVER, a Fortran library for delay differential equations.
leaz/ddeabm
Modern Fortran implementation of the DDEABM Adams-Bashforth algorithm
leaz/decimal_for_cpp
Decimal data type for C++
leaz/DEN-ARMOEA
# Introduction of DNN-AR-MOEA This repository contains code necessary to reproduce the experiments presented in Evolutionary Optimization of High-DimensionalMulti- and Many-Objective Expensive ProblemsAssisted by a Dropout Neural Network. Gaussian processes are widely used in surrogate-assisted evolutionary optimization of expensive problems. We propose a computationally efficient dropout neural network (EDN) to replace the Gaussian process and a new model management strategy to achieve a good balance between convergence and diversity for assisting evolutionary algorithms to solve high-dimensional multi- and many-objective expensive optimization problems. mainlydue to the ability to provide a confidence level of their outputs,making it possible to adopt principled surrogate managementmethods such as the acquisition function used in Bayesian opti-mization. Unfortunately, Gaussian processes become less practi-cal for high-dimensional multi- and many-objective optimizationas their computational complexity is cubic in the number oftraining samples. # References If you found DNN-AR-MOEA useful, we would be grateful if you cite the following reference: Evolutionary Optimization of High-DimensionalMulti- and Many-Objective Expensive ProblemsAssisted by a Dropout Neural Network (IEEE Transactions on Systems, Man and Cybernetics: Systems).
leaz/dop853
Modern Fortran Edition of Hairer's DOP853 ODE Solver
leaz/drake
Model-based design and verification for robotics.
leaz/early-stopping-pytorch
Early stopping for PyTorch
leaz/EMTG
NASA Goddard's Evolutionary Mission Trajectory Generator (EMTG)
leaz/FLINT
Fortran Library for numerical INTegration of differential equations
leaz/fmt
A modern formatting library
leaz/getopt
Simple command-line options handler (C++11)
leaz/gsl
GNU Scientific Library with CMake build support and AMPL bindings
leaz/huhamhire-hosts
hosts for Internet Freedom
leaz/indirect_EM_lyap
Indirect optimization for energy-optimal Lyapunov-to-Lyapunov transfer using heteroclinic connection as initial guess.
leaz/jpl_eph
Code to read, use, and manipulate JPL DE ephemeris data.
leaz/leaz.github.io
My love story with ZhangLe
leaz/liblsoda
The LSODA algorithm for differential equations as a shared library
leaz/matlab2tikz
This program converts MATLAB®/Octave figures to TikZ/pgfplots figures for smooth integration into LaTeX.
leaz/matplotlib-cpp
Extremely simple yet powerful header-only C++ plotting library built on the popular matplotlib
leaz/mercury
N-body fortran integrator
leaz/numpy
Numpy main repository
leaz/OptimTraj
A trajectory optimization library for Matlab
leaz/psopt
PSOPT Optimal Control Software
leaz/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
leaz/smart-o2c
Optimisation and Optimal Control toolbox
leaz/tinyexpr
tiny recursive descent expression parser, compiler, and evaluation engine for math expressions
leaz/tinyformat
Minimal, type safe printf replacement library for C++