Physics-informed machine learning

13 papers with code • 0 benchmarks • 2 datasets

Machine learning used to represent physics-based and/or engineering models

Most implemented papers

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

elizqian/transform-and-learn 17 Dec 2019

The lifting map is applied to data obtained by evaluating a model for the original nonlinear system.

Physics-informed neural networks for corrosion-fatigue prognosis

PML-UCF/pinn Annual Conference of the PHM Society 2019

The result is a cumulative damage model where the physics-informed layers are used to model the relatively well understood physics (crack growth through Paris law) and the data-driven layers account for the hard to model effects (bias in damage accumulation due to corrosion).

A physics-informed neural network for wind turbine main bearing fatigue

PML-UCF/pinn International Journal of Prognostics and Health Management 2020

Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss).

Fleet Prognosis with Physics-informed Recurrent Neural Networks

PML-UCF/pinn 16 Jan 2019

The results demonstrate that our proposed hybrid physics-informed recurrent neural network is able to accurately model fatigue crack growth even when the observed distribution of crack length does not match with the (unobservable) fleet distribution.

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

PV-Lab/BayesProcess 31 Jan 2020

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum.

Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis

oameed/unm_cem_dlfdtd IEEE Open Journal of Antennas and Propagation 2020

In this paper, we propose a deep neural network based model to predict the time evolution of field values in transient electrodynamics.

Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting

JRice15/physics-informed-autoencoders 15 Sep 2020

Accurately predicting sea-surface temperature weeks to months into the future is an important step toward long term weather forecasting.

Physics-Informed Machine Learning Simulator for Wildfire Propagation

MachineLearningJournalClub/MLJC-UniTo-ProjectX-2020-public 12 Dec 2020

The aim of this work is to evaluate the feasibility of re-implementing some key parts of the widely used Weather Research and Forecasting WRF-SFIRE simulator by replacing its core differential equations numerical solvers with state-of-the-art physics-informed machine learning techniques to solve ODEs and PDEs, in order to transform it into a real-time simulator for wildfire spread prediction.

Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems

yeungyh/pickle 30 Jul 2021

In our approach, we extend the physics-informed conditional Karhunen-Lo\'{e}ve expansion (PICKLE) method for modeling subsurface flow with unknown flux (Neumann) and varying head (Dirichlet) boundary conditions.

Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators

Jonas-Nicodemus/PINNs-based-MPC 22 Sep 2021

We thus present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs.