Collection of notebooks about quantitative finance, with interactive python code.
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Updated
Feb 26, 2024 - Jupyter Notebook
Collection of notebooks about quantitative finance, with interactive python code.
Learning in infinite dimension with neural operators.
Simulation and Parameter Estimation in Geophysics - A python package for simulation and gradient based parameter estimation in the context of geophysical applications.
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
FreeFEM source code
Next generation FEniCS problem solving environment
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
FiPy is a Finite Volume PDE solver written in Python
Deep BSDE solver in TensorFlow
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
PDE-Net: Learning PDEs from Data
PyClaw is a Python-based interface to the algorithms of Clawpack and SharpClaw. It also contains the PetClaw package, which adds parallelism through PETSc.
An interactive book about the Riemann problem for hyperbolic PDEs, using Jupyter notebooks.
Grid-based approximation of partial differential equations in Julia
TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).
Finite element toolbox for Julia
PDEBench: An Extensive Benchmark for Scientific Machine Learning
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
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