Collection of notebooks about quantitative finance, with interactive python code.
Learning in infinite dimension with neural operators.
#计算机科学#Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Next generation FEniCS problem solving environment
#计算机科学#PDEBench: An Extensive Benchmark for Scientific Machine Learning
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
Grid-based approximation of partial differential equations in Julia
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Simulation and Parameter Estimation in Geophysics - A python package for simulation and gradient based parameter estimation in the context of geophysical applications.
FiPy is a Finite Volume PDE solver written in Python
Julia package for function approximation
Python package for numerical derivatives and partial differential equations in any number of dimensions.
Python package for solving partial differential equations using finite differences.
Finite element toolbox for Julia
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
#计算机科学#Graph Neural PDEs
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
#计算机科学#PDE-Net: Learning PDEs from Data
18.S096 - Applications of Scientific Machine Learning