graphs-1

Physics-informed machine learning

3260 papers • 126 benchmarks • 313 datasets

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

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in physics-informed-machine-learning-3

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find physics-informed-machine-learning-3 models and implementations

Datasets

CAMELS Multifield Dataset

Expressive Gaussian mixture models for high-dimensional statistical modelling: simulated data and neural network model files

BubbleML

DrivAerNet

Subtasks

Soil moisture estimation

Most implemented papers

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

E. Qian, B. Kramer, B. Peherstorfer, K. Willcox•Mon Dec 16 2019

Lift & Learn is presented, a physics-informed method for learning low-dimensional models for large-scale dynamical systems that are able to capture the system physics in the lifted coordinates at least as accurately as traditional intrusive model reduction approaches.

299

Content

0

Paper Graph

Physics-Informed Neural Networks for Corrosion-Fatigue Prognosis

Arinan Dourado, F. Viana•Sat Sep 21 2019

Results show that the physics-informed neural network is able to learn the correction in the original fatigue model due to corrosion and predictions are accurate enough for ranking damage in different airplanes in the fleet (which can be used to prioritizing inspection).

40 0

Paper Graph

A Physics-informed Neural Network for Wind Turbine Main Bearing Fatigue

F. Viana, Yigit A. Yucesan•Sat Jun 03 2023

Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss). Unfortunately, historical data indicates that failure can happen far earlier than the component design lives. Root cause analysis investigations have pointed to problems inherent from manufacturing as the major contributor, as well as issues related to event loads (e.g., startups, shutdowns, and emergency stops), extreme environmental conditions, and maintenance practices, among others. Altogether, the multiple failure modes and contributors make modeling the remaining useful life of main bearings a very daunting task. In this paper, we present a novel physics-informed neural network modeling approach for main bearing fatigue. The proposed approach is fully hybrid and designed to merge physics-informed and data-driven layers within deep neural networks. The result is a cumulative damage model where the physics-informed layers are used model the relatively well-understood physics (L10 fatigue life) and the data-driven layers account for the hard to model components (i.e., grease degradation).

84 0

Paper Graph

Multi-Objective Loss Balancing for Physics-Informed Deep Learning

Rafael Bischof, M. Kraus•Mon Oct 18 2021

This work proposes a novel self-adaptive loss balancing scheme for PINNs named ReLoBRaLo (Relative Loss Balancing with Random Lookback), and shows that ReLoBRaLo is able to consistently outperform the baseline of existing scaling methods in terms of accuracy, while also inducing significantly less computational overhead.

178 0

Paper Graph

Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis

Oameed Noakoasteen, Shu Wang, Z. Peng, C. Christodoulou•Mon Aug 03 2020

A recurrent neural network based model, which learns representations of long-term spatial-temporal dependencies in the sequence of its input data, and an encoder-recurrent-decoder architecture, which is trained with finite difference time domain simulations of plane wave scattering from distributed, perfect electric conducting objects.

79 0

Paper Graph

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

Felipe Oviedo, T. Buonassisi, Zekun Ren, Maung Thway, S. Tian, Yue Wang, Hansong Xue, J. Darío Perea, Mariya Layurova, Thomas Heumueller, E. Birgersson, A. Aberle, C. Brabec, R. Stangl, Qianxiao Li, Shijing Sun, F. Lin, I. M. Peters•Wed Jul 24 2019

It is demonstrated that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization.

42 0

Paper Graph

Fleet Prognosis with Physics-informed Recurrent Neural Networks

F. Viana, R. Nascimento•Tue Jan 15 2019

The results demonstrate that the 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.

56 0

Paper Graph

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

Julian Rice, Wenwei Xu, Andrew August•Mon Sep 14 2020

It is shown that the Koopman approach consistently outperforms baselines, and the utility of the additional assumptions and methods in this sea-surface temperature domain is discussed.

13 0

Paper Graph

Physics-Informed Machine Learning Simulator for Wildfire Propagation

L. Bottero, Francesco Calisto, Giovanni Graziano, Valerio Pagliarino, Martina Scauda, Sara Tiengo, Simone Azeglio•Fri Dec 11 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.

11 0

Paper Graph

Adding a benchmark result helps the community track progress.