相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。AI-DARWIN: A first principles-based model discovery engine using machine learning
Arijit Chakraborty et al.
COMPUTERS & CHEMICAL ENGINEERING (2021)
Hidden representations in deep neural networks: Part 1. Classification problems
Abhishek Sivaram et al.
COMPUTERS & CHEMICAL ENGINEERING (2020)
Hidden representations in deep neural networks: Part 2. Regression problems
Laya Das et al.
COMPUTERS & CHEMICAL ENGINEERING (2020)
Mechanism discovery and model identification using genetic feature extraction and statistical testing
Arijit Chakraborty et al.
COMPUTERS & CHEMICAL ENGINEERING (2020)
Numerical Differentiation of Noisy Data: A Unifying Multi-Objective Optimization Framework
Floris Van Breugel et al.
IEEE ACCESS (2020)
Explainable Machine Learning for Scientific Insights and Discoveries
Ribana Roscher et al.
IEEE ACCESS (2020)
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2019)
A hierarchical approach for causal modeling of process systems
Resmi Suresh et al.
COMPUTERS & CHEMICAL ENGINEERING (2019)
The promise of artificial intelligence in chemical engineering: Is it here, finally?
Venkat Venkatasubramanian
AICHE JOURNAL (2019)
Data-driven discovery of partial differential equations
Samuel H. Rudy et al.
SCIENCE ADVANCES (2017)
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Steven L. Brunton et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)
Distilling Free-Form Natural Laws from Experimental Data
Michael Schmidt et al.
SCIENCE (2009)
A systematic framework for the development and analysis of signed digraphs for chemical processes. 1. Algorithms and analysis
MR Maurya et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2003)
A systematic framework for the development and analysis of signed digraphs for chemical processes. 2. Control loops and flowsheet analysis
MR Maurya et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2003)
A review of process fault detection and diagnosis Part II: Quantitative model and search strategies
V Venkatasubramanian et al.
COMPUTERS & CHEMICAL ENGINEERING (2003)