论文标题
线性约束的神经网络
Linearly Constrained Neural Networks
论文作者
论文摘要
我们使用明确满足已知线性操作员约束的神经网络来提出一种从物理系统中建模和学习向量领域的新方法。为此，将目标函数建模为基础电势场的线性变换，这又是由神经网络建模的。选择这种转换以使目标函数的任何预测都可以满足约束。该方法在模拟和真实数据示例上都得到了证明。
We present a novel approach to modelling and learning vector fields from physical systems using neural networks that explicitly satisfy known linear operator constraints. To achieve this, the target function is modelled as a linear transformation of an underlying potential field, which is in turn modelled by a neural network. This transformation is chosen such that any prediction of the target function is guaranteed to satisfy the constraints. The approach is demonstrated on both simulated and real data examples.
