AnsleyWong

PINN-for-heat-Transfer

Using PINN's to solve heat transfer problems for parameter

Learning-Python-Physics-Informed-Machine-Learning-PINNs-DeepONets

Physics Informed Machine Learning Tutorials (Pytorch and Jax)

demand_forecast

PINNs-pytorch

PyTorch Implementation of Physics-informed Neural Networks

PINN-s-for-Heat-Transfer-Problem

In recent years, the use of physics-informed neural networks (PINNs) has gained popularity across several engineering disciplines due to their effectiveness in solving linear and non-linear partial differential equations (PDE) and real-world problems despite noisy data. The basic approach used to solve the PINNs is to construct the neural network a

XPINNs

Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

PINNs-TF2.0

TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).

sciann

Deep learning for Engineers - Physics Informed Deep Learning

Gaussian-Processes-Regression-Tutorial

An Intuitive Tutorial to Gaussian Processes Regression

Bidirectional-Deep-readout-Echo-State-Network

Multi-variate time series classification with a bi-directional ESN with a readout implemented as a deep neural network.

thermalNet

thermalNet aims to solve multilayer heat transfer equation with physics-informed neural network.

shap

A game theoretic approach to explain the output of any machine learning model.

bayesianLSTM

Bayesian LSTM (Tensorflow)

deepxde

A library for scientific machine learning and physics-informed learning

PhyLSTM

We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.

sciann-applications

A place to share problems solved with SciANN

KNN-TSPI

K-Nearest Neighbors Time Series Prediction with Invariances

mtl

Unofficial implementation of: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics

Physics-Informed-Neural-Networks

Investigating PINNs

PINN_HeatTransfer_tf2

Soving heat transfer problems using PINN with tf2.0

PINN_Heat_Transfer

PINN for heat transfer problems

LSTM_encoder_decoder

Build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence prediction for time series data

pde-solvers

PDE Solvers: FEM vs Deep Learning

sequential_PINN

Physics-Informed Neural Network (PINN) for Solving Coupled PDEs Governing Thermochemical Physics in Bi-Material Systems

STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

fpn_HSL-TFP

A Surrogate Model with Data Augmentation and Deep Transfer Learning for Temperature Field Prediction of Heat Source Layout

PINNs

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

AutomaticWeightedLoss

Multi-task learning using uncertainty to weigh losses for scene geometry and semantics, Auxiliary Tasks in Multi-task Learning

LSTM-SVM-RF-time-series

Regression prediction of time series data using LSTM, SVM and random forest. 使用LSTM、SVM、随机森林对时间序列数据进行回归预测，注释拉满。

SVSHGP

Stochastic variational heteroscedastic Gaussian process