Physics-informed machine learning
35 papers with code • 0 benchmarks • 4 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
CAMELS Multifield Dataset
Expressive Gaussian mixture models for high-dimensional statistical modelling: simulated data and neural network model files
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
PML-UCF/pinn
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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
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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).
Multi-Objective Loss Balancing for Physics-Informed Deep Learning
rbischof/relative_balancing
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Physics-Informed Neural Networks (PINN) are algorithms from deep learning leveraging physical laws by including partial differential equations together with a respective set of boundary and initial conditions as penalty terms into their loss function.
Physics-informed neural networks for highly compressible flows
wagenaartje/pinn4hcf
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TU Delft Education Repository 2023
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
PML-UCF/pinn
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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
oameed/unm_cem_dlfdtd
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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
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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.
