nkchr123

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/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