Tag: Physics-Informed Neural Networks

ScientificML

This paper studies gradient conflicts and multi-task optimization in Physics-Informed Neural Networks, with implications for more stable convergence in stiff and high-frequency partial differential equations.

Quantum / Chemistry

A probabilistic physics-informed framework using WaveNet and ParamNet to solve the inverse problem of recovering Woods-Saxon nuclear potential parameters from sparse spectral data.

ScientificML

Presents AKAPINN, a mesh-free KAN-PINN method for nonlinear strain-limiting PDEs with exact hard boundary ansatz and pseudo-Huber loss, evaluated across three material-parameter pairs.

CFD/Fluid

This study evaluates physics-informed neural networks for atmospheric potential-temperature forecasting, constrained by pressure-coordinate thermodynamic advection-source equations to address short-horizon predictions under sparse thermal observation conditions.

ScientificML

LPINNs propose multiplying the first hidden activation of a dense PINN by input-dependent localization functions, endowing first-layer units with receptive fields to improve optimization on long domains and for high-order operators without domain partitioning.

Structural

A mesh-free KAN-PINN method for solving nonlinear strain-limiting partial differential equations, featuring exact hard boundary ansatz and pseudo-Huber residual loss for enhanced training stability.

Structural

Investigates the effectiveness of Physics-Informed Neural Networks in simulating transverse vibration of one-dimensional linear stiff strings by incorporating PDE constraints and boundary conditions into the loss function.

ScientificML

Proposes a graph hypernetwork approach using an operator graph that explicitly represents PDE fields, derivatives, terms, and residuals as nodes, enabling amortized neural solver meta-training across related partial differential equations.

ScientificML

This paper proposes PIRNN to enhance interpretability and accuracy of time series forecasting for physical processes by incorporating physical knowledge from historical data during training.

PINN

A heat conduction equation solver based on Physics-Informed Neural Networks, using neural networks embedded with heat conduction physics to predict temperature fields.

CFD

Presents physics-informed hemodynamic modeling for coronary intervention, enabling data-free prediction and sparse-data assimilation to overcome limitations of traditional numerical analysis and purely data-driven approaches.

Scientific Computing

A PINN-related model resource hosted on HuggingFace by OneScience-Group, designed for scientific computing and physics-based problem modeling.

Fourier-PINN

A HuggingFace model repository with a name suggesting Fourier-enhanced Physics-Informed Neural Networks implementation.

Physics-Informed Neural Networks

A HuggingFace repository containing a potential flow PINN clustering model, providing physics-informed neural network resources for potential flow modeling.

PINN Model

A HuggingFace repository hosting a Physics-Informed Neural Network model for hydrostatic fluid mechanics computations, applying physics-based constraints to deep learning approaches.

PINN

A Physics-Informed Neural Network model for solving compressible Euler equations, hosted on HuggingFace.

ScientificML

HIPNO leverages symmetry-aware physics-informed neural operators to infer hemodynamic states from non-invasive signals, addressing scale symmetry challenges in physics-informed hemodynamic inference and expanding access to advanced monitoring.