Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems 论文
2021Physical Review Letters引用 404
Model Reduction and Neural NetworksNeural Networks and ApplicationsFault Detection and Control Systems
详细信息
- 发表期刊/会议
- Physical Review Letters
- 发表日期
- 2021-03-04
- 发表年份
- 2021
关键词
Model Reduction and Neural NetworksNeural Networks and ApplicationsFault Detection and Control Systems
摘要
Neural networks can emulate nonlinear physical systems with high accuracy, yet they may produce physically inconsistent results when violating fundamental constraints. Here, we introduce a systematic way of enforcing nonlinear analytic constraints in neural networks via constraints in the architecture or the loss function. Applied to convective processes for climate modeling, architectural constraints enforce conservation laws to within machine precision without degrading performance. Enforcing constraints also reduces errors in the subsets of the outputs most impacted by the constraints.