# Physics-informed machine learning

28 papers with code • 0 benchmarks • 3 datasets

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

## Benchmarks

These leaderboards are used to track progress in Physics-informed machine learning
## Datasets

## Most implemented papers

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

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

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

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

# Physics-informed neural networks for highly compressible flows

This thesis shows that physics-informed neural networks struggle with highly compressible problems for two independent reasons.

# Fleet Prognosis with Physics-informed Recurrent Neural Networks

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

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

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

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

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

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.