Related references
Note: Only part of the references are listed.Physics-informed machine learning: case studies for weather and climate modelling
K. Kashinath et al.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2021)
Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems
Tom Beucler et al.
PHYSICAL REVIEW LETTERS (2021)
Machine Learning for Molecular Simulation
Frank Noé et al.
Annual Review of Physical Chemistry (2020)
Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems
Jin-Long Wu et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2020)
Al Feynman: A physics-inspired method for symbolic regression
Silviu-Marian Udrescu et al.
SCIENCE ADVANCES (2020)
Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models
Timur Bikmukhametov et al.
COMPUTERS & CHEMICAL ENGINEERING (2020)
Turbulence Modeling in the Age of Data
Karthik Duraisamy et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 51 (2019)
Projection-based model reduction: Formulations for physics-based machine learning
Renee Swischuk et al.
COMPUTERS & FLUIDS (2019)
Guiding new physics searches with unsupervised learning
Andrea De Simone et al.
EUROPEAN PHYSICAL JOURNAL C (2019)
Machine Learning Derived Quantitative Structure Property Relationship (QSPR) to Predict Drug Solubility in Binary Solvent Systems
Sivadurgaprasad Chinta et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2019)
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)
An artificial neural network framework for reduced order modeling of transient flows
Omer San et al.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION (2019)
The promise of artificial intelligence in chemical engineering: Is it here, finally?
Venkat Venkatasubramanian
AICHE JOURNAL (2019)
Gray-Box Modeling for the Optimization of Chemical Processes
Norbert Asprion et al.
CHEMIE INGENIEUR TECHNIK (2019)
Modeling and optimization of wet flue gas desulfurization system based on a hybrid modeling method
Yishan Guo et al.
JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (2019)
Machine learning and the physical sciences
Giuseppe Carleo et al.
REVIEWS OF MODERN PHYSICS (2019)
Statistical Mechanics of Deep Learning
Yasaman Bahri et al.
Annual Review of Condensed Matter Physics (2019)
Designing neural networks through neuroevolution
Kenneth O. Stanley et al.
NATURE MACHINE INTELLIGENCE (2019)
Automating drug discovery
Gisbert Schneider
NATURE REVIEWS DRUG DISCOVERY (2018)
Sparse identification of nonlinear dynamics for rapid model recovery
Markus Quade et al.
CHAOS (2018)
Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers
Mohammad Atif Faiz Afzal et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Accelerating the discovery of materials for clean energy in the era of smart automation
Daniel P. Tabor et al.
NATURE REVIEWS MATERIALS (2018)
Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review
Sohrab Zendehboudi et al.
APPLIED ENERGY (2018)
Identification of Errors-in-Variables Models Using Dynamic Iterative Principal Component Analysis
Deepak Maurya et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2018)
Data-Driven Discovery of Closure Models
Shaowu Pan et al.
SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS (2018)
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Waseem Rawat et al.
NEURAL COMPUTATION (2017)
Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function
Michael W. Reimann et al.
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE (2017)
Data-driven discovery of partial differential equations
Samuel H. Rudy et al.
SCIENCE ADVANCES (2017)
Inverse statistical problems: from the inverse Ising problem to data science
H. Chau Nguyen et al.
ADVANCES IN PHYSICS (2017)
Biodiesel production from castor oil: ANN modeling and kinetic parameter estimation
Atiya Banerjee et al.
INTERNATIONAL JOURNAL OF INDUSTRIAL CHEMISTRY (2017)
Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead
Fatai Adesina Anifowose et al.
JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY (2017)
Hybrid modeling as a QbD/PAT tool in process development: an industrial E-coli case study
Moritz von Stosch et al.
BIOPROCESS AND BIOSYSTEMS ENGINEERING (2016)
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
Julia Ling et al.
JOURNAL OF FLUID MECHANICS (2016)
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)
Deconstructing principal component analysis using a data reconciliation perspective
Shankar Narasimhan et al.
COMPUTERS & CHEMICAL ENGINEERING (2015)
A combined first-principles and data-driven approach to model building
Alison Cozad et al.
COMPUTERS & CHEMICAL ENGINEERING (2015)
A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth
Xing-Rong Fan et al.
ECOLOGICAL MODELLING (2015)
Hybrid modeling for quality by design and PAT - benefits and challenges of applications in biopharmaceutical industry
Moritz von Stosch et al.
BIOTECHNOLOGY JOURNAL (2014)
Hybrid semi-parametric modeling in process systems engineering: Past, present and future
Moritz von Stosch et al.
COMPUTERS & CHEMICAL ENGINEERING (2014)
Proper orthogonal decomposition closure models for turbulent flows: A numerical comparison
Zhu Wang et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2012)
A general hybrid semi-parametric process control framework
Moritz von Stosch et al.
JOURNAL OF PROCESS CONTROL (2012)
Identification of semi-parametric hybrid process models
Aidong Yang et al.
COMPUTERS & CHEMICAL ENGINEERING (2011)
A novel identification method for hybrid (N)PLS dynamical systems with application to bioprocesses
M. von Stosch et al.
EXPERT SYSTEMS WITH APPLICATIONS (2011)
Hybrid modeling of ethylene to ethylene oxide heterogeneous reactor
G. Zahedi et al.
FUEL PROCESSING TECHNOLOGY (2011)
Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage
Guangyi Chen et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2011)
Prediction of Drug Solubility in Mixed Solvent Systems Using the COSMO-SAC Activity Coefficient Model
Chun-Chieh Shu et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2011)
Solubility Prediction of Drugs in Mixed Solvents Using Partial Solubility Parameters
Abolghasem Jouyban et al.
JOURNAL OF PHARMACEUTICAL SCIENCES (2011)
NEURAL NETWORK-BASED HEAT AND MASS TRANSFER COEFFICIENTS FOR THE HYBRID MODELING OF FLUIDIZED REACTORS
Farouq S. Mjalli et al.
CHEMICAL ENGINEERING COMMUNICATIONS (2010)
Two-stage image denoising by principal component analysis with local pixel grouping
Lei Zhang et al.
PATTERN RECOGNITION (2010)
MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS
Yehuda Koren et al.
COMPUTER (2009)
Distilling Free-Form Natural Laws from Experimental Data
Michael Schmidt et al.
SCIENCE (2009)
Model identification and error covariance matrix estimation from noisy data using PCA
Shankar Narasimhan et al.
CONTROL ENGINEERING PRACTICE (2008)
Sigma profile database for predicting solid solubility in pure and mixed solvent mixtures for organic pharmacological compounds with COSMO-based thermodynamic methods
Eric Mullins et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2008)
A grey-box modeling approach for the reduction of nonlinear systems
Reinout Romijn et al.
JOURNAL OF PROCESS CONTROL (2008)
Incorporating prior knowledge in support vector regression
Fabien Lauer et al.
MACHINE LEARNING (2008)
The validity domain of hybrid models and its application in process optimization
O. Kahrs et al.
CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION (2007)
A semi-mechanistic model building framework based on selective and localized model extensions
Pedro V. Lima et al.
COMPUTERS & CHEMICAL ENGINEERING (2007)
Dynamic hybrid neural network model of an industrial fed-batch fermentation process to produce foreign protein
Siris O. Laursen et al.
COMPUTERS & CHEMICAL ENGINEERING (2007)
First-principles, data-based, and hybrid modeling and optimization of an industrial hydrocracking unit
N. Bhutani et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2006)
Modeling of a three-phase industrial batch reactor using a hybrid first-principles neural-network model
Levente L. Simon et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2006)
Integrating knowledge-driven and data-driven approaches to modeling
L Todorovski et al.
ECOLOGICAL MODELLING (2006)
Modelling of the performance of industrial HDS reactors using a hybrid neural network approach
GD Bellos et al.
CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION (2005)
Hybrid neural network-prior knowledge model in temperature control of a semi-batch polymerization process
CW Ng et al.
CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION (2004)
Incorporating prior knowledge into artificial neural networks - an industrial case study
S Milanic et al.
NEUROCOMPUTING (2004)
Hybrid process models for process optimisation, monitoring and control
V Galvanauskas et al.
BIOPROCESS AND BIOSYSTEMS ENGINEERING (2004)
Combining first principles modelling and artificial neural networks: a general framework
R Oliveira
COMPUTERS & CHEMICAL ENGINEERING (2004)
Hybrid model-based approach to batch-to-batch control of particle size distribution in emulsion polymerization
FJ Doyle et al.
COMPUTERS & CHEMICAL ENGINEERING (2003)
Hybrid neural network modeling of a full-scale industrial wastewater treatment process
DS Lee et al.
BIOTECHNOLOGY AND BIOENGINEERING (2002)
Grey-box modelling and control of chemical processes
Q Xiong et al.
CHEMICAL ENGINEERING SCIENCE (2002)
Comparison of different methods for variable selection
L Xu et al.
ANALYTICA CHIMICA ACTA (2001)
Prediction of drug solubility by the general solubility equation (GSE)
YQ Ran et al.
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES (2001)
Gray-box identification of block-oriented nonlinear models
RK Pearson et al.
JOURNAL OF PROCESS CONTROL (2000)
Share Paper
