物理信息机器学习
将 Deep Learning Toolbox™ 用于物理信息机器学习 (PIML) 和物理信息神经网络 (PINN)。
物理信息机器学习 (PIML) 和物理信息神经网络指的是特定的机器学习和深度学习概念，在这些概念中，您可以将物理系统的定律和原理整合到您的机器学习模型中。整合这些概念可以提高这些模型的准确度和稳健性，并有助于确保模型预测也遵循这些定律和原理。例如，您可以使用纳入热力学定律的损失函数来训练对热传递进行建模的神经网络。
函数
主题
- Solve PDE Using Fourier Neural Operator
This example shows how to train a Fourier neural operator (FNO) neural network that outputs the solution of a partial differential equation (PDE).
- Solve PDE Using Physics-Informed Neural Network
This example shows how to train a physics-informed neural network (PINN) to predict the solutions of an partial differential equation (PDE).
- Solve ODE Using Physics-Informed Neural Network
This example shows how to train a physics-informed neural network (PINN) to predict the solutions of an ordinary differential equation (ODE).
- Train Latent ODE Network with Irregularly Sampled Time-Series Data
This example shows how to train a latent ordinary differential equation (ODE) autoencoder with time-series data that is sampled at irregular time intervals.
- Dynamical System Modeling Using Neural ODE
This example shows how to train a neural network with neural ordinary differential equations (ODEs) to learn the dynamics of a physical system.
- Solve Inverse Problem for PDE Using Physics-Informed Neural Network
This example shows how to solve an inverse problem using a physics-informed neural network (PINN).
- Solve Poisson Equation on Unit Disk Using Physics-Informed Neural Networks (Partial Differential Equation Toolbox)
Solve a Poisson's equation with Dirichlet boundary conditions using a physics-informed neural network (PINN).
精选示例
Solve Heat Equation Using Graph Neural Network
Solve a heat equation with using a graph neural network (GNN).
