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-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.
Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators
Jonas-Nicodemus/PINNs-based-MPC
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We thus present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs.
Physics informed machine learning with Smoothed Particle Hydrodynamics: Hierarchy of reduced Lagrangian models of turbulence
Within this hierarchy, two new parameterized smoothing kernels are developed in order to increase the flexibility of the learn-able SPH simulators.
Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment
AbhilashMathews/PlasmaPINNtheory
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With this experimentally inferred data, initial estimates of the 2-dimensional turbulent electric field consistent with drift-reduced Braginskii theory under the framework of an axisymmetric fusion plasma with purely toroidal field are calculated.
Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management
To tackle this, we use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization at critical reservoir locations.
Noise-aware Physics-informed Machine Learning for Robust PDE Discovery
nPIML-team/nPIML
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This work is concerned with discovering the governing partial differential equation (PDE) of a physical system.
Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures
zlaidyn/neural-modal-ode-demo
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In the scope of physics-informed machine learning, this paper proposes a framework -- termed Neural Modal ODEs -- to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems.
STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction
echo-ji/stden
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High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities.
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference
We propose the use of HNNs for performing Bayesian inference efficiently without requiring numerous posterior gradients.
Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
i207m/pinnacle
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Recent work shows that it provides potential benefits for machine learning models by incorporating the physical prior and collected data, which makes the intersection of machine learning and physics become a prevailing paradigm.
