scope
NeuralPDE.jl
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scope | NeuralPDE.jl | |
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4 | 10 | |
5,811 | 891 | |
0.1% | 2.9% | |
0.0 | 9.7 | |
9 months ago | 6 days ago | |
Go | Julia | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
scope
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IA et Calcul scientifique dans Kubernetes avec le langage Julia, K8sClusterManagers.jl
Weave Scope
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Interactive Architecture Diagrams
There are products that will introspect a k8s cluster and give a diagram like: https://www.weave.works/oss/scope/
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Kubernetes Security Checklist 2021
Build observability and visibility processes in order to understand what is happening in infrastructure and services (Luntry, WaveScope)
NeuralPDE.jl
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from Wolfram Mathematica to Julia
PDE solving libraries are MethodOfLines.jl and NeuralPDE.jl. NeuralPDE is very general but not very fast (it's a limitation of the method, PINNs are just slow). MethodOfLines is still somewhat under development but generates quite fast code.
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IA et Calcul scientifique dans Kubernetes avec le langage Julia, K8sClusterManagers.jl
GitHub - SciML/NeuralPDE.jl: Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
NeuralPDE.jl: Scientific Machine Learning for Partial Differential Equations
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[Research] Input Arbitrary PDE -> Output Approximate Solution
PDEs are difficult because you don't have a simple numerical definition over all PDEs because they can be defined by arbitrarily many functions. u' = Laplace u + f? Define f. u' = g(u) * Laplace u + f? Define f and g. Etc. To cover the space of PDEs you have to go symbolic at some point, and make the discretization methods dependent on the symbolic form. This is precisely what the ModelingToolkit.jl ecosystem is doing. One instantiation of a discretizer on this symbolic form is NeuralPDE.jl which takes a symbolic PDESystem and generates an OptimizationProblem for a neural network which represents the solution via a Physics-Informed Neural Network (PINN).
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[D] Has anyone worked with Physics Informed Neural Networks (PINNs)?
NeuralPDE.jl fully automates the approach (and extensions of it, which are required to make it solve practical problems) from symbolic descriptions of PDEs, so that might be a good starting point to both learn the practical applications and get something running in a few minutes. As part of MIT 18.337 Parallel Computing and Scientific Machine Learning I gave an early lecture on physics-informed neural networks (with a two part video) describing the approach, how it works and what its challenges are. You might find those resources enlightening.
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Doing Symbolic Math with SymPy
What is great about ModelingToolkit.jl is how its used in practical ways for other packages. E.g. NeuralPDE.jl.
Compared to SymPy, I feel that it is less of a "how do I integrate this function" package and more about "how can I build this DSL" framework.
What are some alternatives?
deepxde - A library for scientific machine learning and physics-informed learning
SymPy - A computer algebra system written in pure Python
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
ReservoirComputing.jl - Reservoir computing utilities for scientific machine learning (SciML)
lynis - Lynis - Security auditing tool for Linux, macOS, and UNIX-based systems. Assists with compliance testing (HIPAA/ISO27001/PCI DSS) and system hardening. Agentless, and installation optional.
AMDGPU.jl - AMD GPU (ROCm) programming in Julia
18337 - 18.337 - Parallel Computing and Scientific Machine Learning
Gridap.jl - Grid-based approximation of partial differential equations in Julia
falco - Cloud Native Runtime Security
Pyston - A faster and highly-compatible implementation of the Python programming language.
auto-07p - AUTO is a publicly available software for continuation and bifurcation problems in ordinary differential equations originally written in 1980 and widely used in the dynamical systems community.
branca - :key: Secure alternative to JWT. Authenticated Encrypted API Tokens for Go.