Catalyst.jl
casadi
Catalyst.jl | casadi | |
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2 | 4 | |
422 | 1,554 | |
1.4% | 1.7% | |
9.5 | 9.3 | |
6 days ago | 3 days ago | |
Julia | C++ | |
GNU General Public License v3.0 or later | GNU Lesser General Public License v3.0 only |
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Catalyst.jl
- Julia macros
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Should I switch over completely to Julia from Python for numerical analysis/computing?
ModelingToolkit.jl adds a different spin on this by noting what makes a good modeling system isn't top down but a system that allows for bottom up contributions. ModelingToolkit is built on Symbolics.jl which uses OSCAR.jl etc., so every time the symbolics community gets better ModelingToolkit.jl gets better. It connects to the whole SciML ecosystem, so any improvement to any of the SciML interface packages is directly an improvement to ModelingToolkit.jl. ModelingToolkit is made to be a set of composable compiler abstractions called transformations, so anyone can add new packages that do new transformations that improve the ecosystem. One that I really like is MomentClosure.jl which symbolically transforms stochastic ModelingToolkit models (ReactionSystem) to approximate symbolic ODESystem models of the moments. And there's domain-specific langauges like Catalyst.jl being built on the interface to give more ways to build models, which is spawning the biocommunity to make model importers into the symbolic forms, when then feeds more ODE models into the same compiler. JuliaSim is then building on this ecosystem, adding cloud infrastructure that is special-purpose made for doing parallel computations of these models, automatic symbolic model discovery from data, automatic generation of approximate models with machine learning, and tying the Julia Computing compiler team into the web that is building this ecosystem.
casadi
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pyomo VS casadi - a user suggested alternative
2 projects | 5 Sep 2023
Interface for several solvers and integrators.
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(Direct) Collocation in (Time) Optimal Control
Howdy! Collocation methods can be... tricky. For NMPC control of vehicles, success has been had using direct multiple shooting. Also easier to implement and more intuitive. In fact, this example from the GH is pretty instructive: https://github.com/casadi/casadi/blob/master/docs/examples/python/race_car.py
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Are there any optimization libraries/packages that use automatic differentiation?
JuMP.jl (Julia) or casADi (Python) are good choices.
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Should I switch over completely to Julia from Python for numerical analysis/computing?
Python is not mature in this area. If you ask Google what Simulink for Python is, you get responses that point to dead libraries that were never feature complete and slow. The absolute closest is CASADI which is nice for some things but doesn't even have a true causal modeling interface and is mostly abandoned by the developers (they put a patch in every now and then, but just look at the commit graph), and it's slow compared to the Julia tools, so much so that PyBAMM is interfacing with ModelingToolkit.jl in Julia for a performance boost. Python is not the place to be for causal/acausal modeling or controls. Anyone who is saying "Python is mature" here is saying it in the abstract and not in the context of your actual question. Yes, Python has web development frameworks. No it does not have good libraries for tons of areas (control, acausal modeling, pharmacometrics, etc.).
What are some alternatives?
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
ceres-solver - A large scale non-linear optimization library
ParameterizedFunctions.jl - A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
MuladdMacro.jl - This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem
symbolic - A Symbolic Package for Octave using SymPy
JuMP.jl - Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
jsbsim - An open source flight dynamics & control software library
Causal.jl - Causal.jl - A modeling and simulation framework adopting causal modeling approach.
symforce - Fast symbolic computation, code generation, and nonlinear optimization for robotics
MomentClosure.jl - Tools to generate and study moment equations for any chemical reaction network using various moment closure approximations
fricas - Official repository of the FriCAS computer algebra system