kjwoxinyijiu

kjwoxinyijiu's Stars

• MilesCranmer/PySR

  High-Performance Symbolic Regression in Python and Julia

  Language:Python2.3k211

• EmilienDupont/augmented-neural-odes

  Pytorch implementation of Augmented Neural ODEs :sunflower:

  Language:Jupyter Notebook52788

• Rachnog/Neural-ODE-Experiments

  This repository contains experiments with Neural Ordinary Differential Equations with simulated and real empirical data

  Language:Jupyter Notebook19561

• GitTeaching/Predicting-using-Neural-ODE

  Deep Learning - Predicting using Neural Ordinary Differential Equations - torchdiffeq.

  Language:Jupyter Notebook142

• feicccccccc/Neural-ODE-Ex

  Exercise for implementing neural ODE

  Language:Python4

• upb-lea/Inkscape_electric_Symbols

  Electrical symbol library for the vector graphics program Inkscape.

  37954

• yangzhen0512/IntelligentOptimizationAlgorithms

  This repository displays the demos of some Intelligent Optimization Algorithms, including SA (Simulated Annealing), GA (Genetic algorithm), PSO (Particle Swarm Optimizer) and so on. And some other algorithms will be appended in the future.

  Language:Matlab24267

• shap/shap

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

  Language:Jupyter Notebook22.6k3.3k

• ms140429/pfNDM

  Explainable neural dynamic model for motor temperature prediction

  Language:C5

• teslamotors/roadster

  2008-2012 Roadster Development and Diagnostic Software files

  1k292

• advaitsave/Introduction-to-Time-Series-forecasting-Python

  Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.

  Language:Jupyter Notebook315136

• crewsdw/pinns_project

  Repository for class project on discrete-time physics-informed neural network solver

  Language:Python21

• allenxcao/PINNs

  Language:Jupyter Notebook6

• pnnl/neuromancer

  Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

  Language:Python887116

• chueh-ermon/automate-Arbin-schedule-file-creation

  Arbin schedule file automation from CSV

  Language:Python3413

• chueh-ermon/BMS-autoanalysis

  Language:MATLAB4320

• chueh-ermon/battery-fast-charging-optimization

  This repository contains much of the data, code, and visualizations associated with this manuscript:

  Language:Python8633

• rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation

  Code for Nature energy manuscript

  Language:Jupyter Notebook477195

• saniaki/sequential_PINN

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

  Language:Jupyter Notebook4617

• jdtoscano94/Learning-Scientific_Machine_Learning_Residual_Based_Attention_PINNs_DeepONets

  Physics Informed Machine Learning Tutorials (Pytorch and Jax)

  Language:Jupyter Notebook436164

• aamini/evidential-deep-learning

  Learn fast, scalable, and calibrated measures of uncertainty using neural networks!

  Language:Python43197

• Jonas-Nicodemus/PINNs-based-MPC

  We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising tool to approximate (partial) differential equations. PINNs are not suited for control tasks in their original form since they are not designed to handle variable control actions or variable initial values. We thus present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs. The high-dimensional input space is subsequently reduced via a sampling strategy and a zero-hold assumption. This strategy enables the controller design based on a PINN as an approximation of the underlying system dynamics. The additional benefit is that the sensitivities are easily computed via automatic differentiation, thus leading to efficient gradient-based algorithms. Finally, we present our results using our PINN-based MPC to solve a tracking problem for a complex mechanical system, a multi-link manipulator.

  Language:Python7719

• nmarcelo/Physics_Informed_NN

  Physics Informed Neural Network

  Language:MATLAB4

• janblechschmidt/PDEsByNNs

  This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means of neural networks using TensorFlow.

  Language:Jupyter Notebook15563

• Crunch-UQ4MI/neuraluq

  Language:Python15033

• VNemani14/UQ_ML_Tutorial

  Code repository for review paper titled "Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Comprehensive Review"

  Language:Jupyter Notebook245

• benmoseley/FBPINNs

  Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)

  Language:Python30060

• levimcclenny/SA-PINNs

  Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]

  Language:Python16536

• nanditadoloi/PINN

  Simple PyTorch Implementation of Physics Informed Neural Network (PINN)

  Language:Jupyter Notebook23945

• PredictiveIntelligenceLab/USNCCM15-Short-Course-Recent-Advances-in-Physics-Informed-Deep-Learning

  Language:Jupyter Notebook11559