Computational Science and Engineering
物理信息代理模型
Physics-informed surrogatesA data-driven modeling approach that integrates physical laws and governing equations into machine learning models to improve accuracy and generalizability. / 一种将物理定律和控制方程集成到机器学习模型中的数据驱动建模方法，以提高准确性和泛化能力。
- Knowledge type
- 工艺技术
- First proposed or discovered
- Proposer
- Turkey Farm member
- Creator
- 农场主
- Last updated
- 2026-11-07 23:06:18
Sub-knowledge points
Intelligence-enabled smart inhalable biomaterials / 实际应用Multi-objective optimizationReactor design and scaling
Higher-order knowledge points
Main parameters and performance indicators
Physics-informed loss function: balances data fidelity and physical constraintsSurrogate model architecture: e.g., neural networks or Gaussian processes
Open problems
No unresolved problem recorded yet
Related literature
ranked by community starsPhysics-informed surrogates for multi-objective optimization of scraped falling-film plastic pyrolysis reactorsOpen source
Related products and links
No item recorded yet
Creation and modification trail
1 recordsRecord title: Physics-informed surrogates.
Node update feedback
review required