A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
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Updated
Jul 2, 2024 - Python
A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
Physics-Informed Neural networks for Advanced modeling
Code for the paper "Learning Differential Equations that are Easy to Solve"
Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.
A differentiable physics engine and multibody dynamics library for control and robot learning.
odeintw provides a wrapper of scipy.integrate.odeint that allows it to handle complex and matrix differential equations.
A Python Framework for Modeling and Analysis of Signaling Systems
A Python library for solving Initial Value Problems using various numerical integration methods.
SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations
Python toolbox to detect limit cycles and asses their stability
ODE system solver using dG(q), time-discontinuous Galerkin with Lobatto basis.
Extend scipy.integrate with various methods for solve_ivp
P. Petsagkourakis, I. O. Sandoval, E. Bradford, D. Zhang, E.A. del Rio-Chanona, Reinforcement learning for batch bioprocess optimization, Computers & Chemical Engineering, Volume 133, 2020
Stochastic Cellular Automata epidemic models in Python with 2D simulations
A solver for Boson Stars using a shooting method to calculate solutions
MOCCASIN translates basic ODE-based MATLAB models of biological processes into SBML format.
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