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
Physics-constrained deep learning postprocessing of temperature and humidity
Weather forecasting centers currently rely on statistical postprocessing methods to minimize forecast error.
Π-ML: A dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layer
Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams.
Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent Organization
Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium.
An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential Equations
ai-research-disi/ode-discovery-with-ude
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In the last decade, the scientific community has devolved its attention to the deployment of data-driven approaches in scientific research to provide accurate and reliable analysis of a plethora of phenomena.
A Machine Learning Pressure Emulator for Hydrogen Embrittlement
A recent alternative for hydrogen transportation as a mixture with natural gas is blending it into natural gas pipelines.
Neural oscillators for generalization of physics-informed machine learning
taniyakapoor/AAAI24_Generalization_PIML
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A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented by partial differential equations (PDEs).
Hyperspectral Blind Unmixing using a Double Deep Image Prior
ChaoEdisonZhouUCL/BUDDIP-TNNLS
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IEEE Transactions on Neural Networks and Learning Systems 2023
With the rise of machine learning, hyperspectral image (HSI) unmixing problems have been tackled using learning-based methods.
Separable Hamiltonian Neural Networks
Hamiltonian neural networks (HNNs) are state-of-the-art models that regress the vector field of a dynamical system under the learning bias of Hamilton's equations.
Estimating irregular water demands with physics-informed machine learning to inform leakage detection
swn-group-at-tu-berlin/lila-pinn
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Our algorithm is tested on data from the L-Town benchmark network, and results indicate a good capability for estimating most irregular demands, with R2 larger than 0. 8.
Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning
stfc-sciml/zerocoordinateshift
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Automatic differentiation (AD) is a critical step in physics-informed machine learning, required for computing the high-order derivatives of network output w. r. t.
