Pinned Repositories
2DStefan
AtPINN
code
BPINNs
Data-driven-pressure-estimation-with-EPOD
DeepStefan
DeepTurbulence
heat-pinn
A Physics-Informed Neural Network to solve 2D steady-state heat equation.
Li-ion-Battery-Prognosis-Based-on-Hybrid-Bayesian-PINN
Code used to generate the results of the paper: Nascimento et al. A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks.
Optimization-of-building-energy-consumption
This repo demonstrates how to build a surrogate (proxy) model by multivariate regressing building energy consumption data (univariate and multivariate) and use (1) Bayesian framework, (2) Pyomo package, (3) Genetic algorithm with local search, and (4) Pymoo package to find optimum design parameters and minimum energy consumption.
PINN_Heat_Transfer
PINN for heat transfer problems
nkchr123's Repositories
nkchr123/PINN_Heat_Transfer
PINN for heat transfer problems
nkchr123/2DStefan
nkchr123/AtPINN
code
nkchr123/BPINNs
nkchr123/Data-driven-pressure-estimation-with-EPOD
nkchr123/DeepStefan
nkchr123/DeepTurbulence
nkchr123/heat-pinn
A Physics-Informed Neural Network to solve 2D steady-state heat equation.
nkchr123/Li-ion-Battery-Prognosis-Based-on-Hybrid-Bayesian-PINN
Code used to generate the results of the paper: Nascimento et al. A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks.
nkchr123/Optimization-of-building-energy-consumption
This repo demonstrates how to build a surrogate (proxy) model by multivariate regressing building energy consumption data (univariate and multivariate) and use (1) Bayesian framework, (2) Pyomo package, (3) Genetic algorithm with local search, and (4) Pymoo package to find optimum design parameters and minimum energy consumption.
nkchr123/para-database-for-PIML
nkchr123/Physics-constrained-Bayesian-deep-learning
Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data
nkchr123/PINN-for-turbulence
A pytorch implementation of several approaches using PINN to slove turbulent flow
nkchr123/PINN-laminar-flow
Physics-informed neural network for solving fluid dynamics problems
nkchr123/pinn-torch
PINN (Physics-Informed Neural Networks) on Navier-Stokes Equations
nkchr123/PINN4SOH
A physics-informed neural network for battery SOH estimation
nkchr123/PINN_HeatTransfer_tf2
Soving heat transfer problems using PINN with tf2.0
nkchr123/PINN_TF2
Implementation of PINNs in TensorFlow 2
nkchr123/PINNs
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
nkchr123/PINNs-1
nkchr123/rans-uncertainty
Uncertainty Quantification of RANS Data-Driven Turbulence Modeling
nkchr123/ShallowDecoder
Shallow Learning for Flow Reconstruction with Limited Sensors and Limited Data
nkchr123/Stefan
A course project to assess several numerical solutions to the Stefan problem for heat transfer in a two-phase material
nkchr123/xPINNs
when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/10.1016/j.cma.2022.115346