HediChen's Stars
HangGaoNUDT/HazardousWindDetection
reecehuff/StrainNet
StrainNet is a deep neural network for measuring deformation from images
ColinLii/CC-FJpy
A Python Package for seismic ambient noise cross-correlation and the frequency-Bessel transform method
ShaoboYang-USTC/EGFAnalysisPy
基于图像分析方法,从经验格林函数或互相关函数中计算面波群速度及相速度频散能量图,进而使用基于人工智能的频散曲线提取方法DisperPicker从频散能量图中自动提取频散曲线。
jxensing/ftanpy
Frequency-Time ANalysis (FTAN) in python
ShaoboYang-USTC/DisperPicker
Automatically extracting dispersion curves via deep learning
martijnende/jDAS
Coherence-based Deep Learning denoising of DAS data
noisepy/NoisePy
Ambient-Noise Seismology Package
WenjieDu/PyPOTS
A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/classification/clustering/forecasting/anomaly detection/cleaning on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values
Guillem96/madgan-pytorch
Multivariate Anomaly Detection with GAN (MAD-GAN) PyTorch modern implementation.
LiDan456/MAD-GANs
Applied generative adversarial networks (GANs) to do anomaly detection for time series data
ashiq24/UNO
DedalusProject/dedalus
A flexible framework for solving PDEs with modern spectral methods.
katiana22/latent-deeponet
Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."
KTH-FlowAI/Identifying-regions-of-importance-in-wall-bounded-turbulence-through-explainable-deep-learning
Code for reproducing the article: Identifying regions of importance in wall-bounded turbulence through explainable deep learning.
Ali-Stanford/PointNetCFD
Point-cloud deep learning for prediction of fluid flow fields on irregular geometries (supervised learning)
lululxvi/deepxde
A library for scientific machine learning and physics-informed learning
lululxvi/tutorials
Tutorials on deep learning, Python, and dissipative particle dynamics
sciann/sciann
Deep learning for Engineers - Physics Informed Deep Learning
sciann/sciann-applications
A place to share problems solved with SciANN
raj-brown/APMA_2070_ENGN_2912_SPRING_2024
lululxvi/deeponet
Learning nonlinear operators via DeepONet
neuraloperator/Geo-FNO
Geometry-Aware Fourier Neural Operator (Geo-FNO)
neuraloperator/physics_informed
neuraloperator/graph-pde
Using graph network to solve PDEs
neuraloperator/neuraloperator
Learning in infinite dimension with neural operators.
LiuYangMage/in-context-operator-networks
ICON for in-context operator learning
idrl-lab/PINNpapers
Must-read Papers on Physics-Informed Neural Networks.
gstinoco/Cloud-Generation
Generate regular or irregular clouds of points from contours or meshes.
gstinoco/mGFD
Meshless Generalized Finite Differences for numerically solve Partial Differential Equations