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Code for performing adversarial attacks on atomistic systems using NN potentials
Must-read Papers on Physics-Informed Neural Networks.
A package for computing data-driven approximations to the Koopman operator.
A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold N…
Finds the committor function, the reactive current, and the transition rate for 2D problems using finite element method on mesh generated by P.-O. Persson's distmesh algorithm
PINN (Physics-Informed Neural Networks) on Navier-Stokes Equations
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Utility functions that GPU-Optional Python Code
A python package to locate poles and zeros of a meromorphic function with their multiplicities
A Julia Implementation of a Parallel in Time ODE Solver
Sparse Grid Discretization with the Discontinuous Galerkin Method for solving PDEs
Survey of the packages of the Julia ecosystem for solving partial differential equations
Grid-based approximation of partial differential equations in Julia
FEMBasis contains interpolation routines for finite element function spaces. Given ansatz and coordinates of domain, shape functions are calculated symbolically in a very general way to get efficie…
Variational Autoencoder for Dimensionality Reduction of Time-Series
Deep learning meets molecular dynamics.
Maximum Likelihood estimation and Simulation for Stochastic Differential Equations (Diffusions)
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.