florencewuyixuan

florencewuyixuan's Stars

• apple/ml-robust-expert-augmentations

  Language:Python25

• n-takeishi/phys-vae

  Language:Python113

• LaurentNevou/Q_Schrodinger2D_demo

  2D Time independent Schroedinger equation solver

  Language:MATLAB2811

• amazon-science/probconserv

  Datasets and code for results presented in the ProbConserv paper

  Language:Python519

• bitzhangcy/Neural-PDE-Solver

  726106

• rgp62/cvpinns

  Language:Jupyter Notebook85

• AmeyaJagtap/Conservative_PINNs

  We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation property of cPINN is obtained by enforcing the flux continuity in the strong form along the sub-domain interfaces.

  Language:Python5519

• dynamicslab/pysindy

  A package for the sparse identification of nonlinear dynamical systems from data

  Language:Python1.4k312

• paulpuren/PhyCRNet

  Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

  Language:Python215

• snagcliffs/PDE-FIND

  Language:Jupyter Notebook233119

• dynamicslab/SINDy-PI

  SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics

  Language:MATLAB12942

• paulpuren/PhySR

  Physics-informed deep super-resolution of spatiotemporal data

  Language:Python324

• isds-neu/PhyCRNet

  Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

  Language:Python11734

• coscholz1984/GS_CNN

  Scripts for exploring complex pattern formation in the Gray-Scott model using convolutional neural networks

  Language:Python31

• YinSifan0204/Active-Matter-HW

  Problem solutions to "Active Matter" (Spring 2023)

  Language:Python1

• Raocp/PeRCNN

  Physics-encoded recurrent convolutional neural network

  Language:Python407

• Jianxun-Wang/phygeonet

  PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain

  Language:C++8030

• ehsankharazmi/PINN-COVID

  PINN-COVID analyzes a plurality of epidemiological models through the lens of physics-informed neural networks (PINNs).

  Language:Python177

• wuwushrek/physics_constrained_nn

  Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling

  Language:Python405

• wimvanrees/growth_SM2018

  code accompanying the 2018 Soft Matter paper "Mechanics of biomimetic 4D printed structures"

  Language:C++115

• isds-neu/PeRCNN

  Encoding physics to learn reaction-diffusion processes

  Language:Python8418