Functional-Hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressions

Principal component‐guided sparse regression

(2021) Jingyi K. Tay et al.   CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE

Hybrid Models for the simulation and prediction of chromatographic processes for protein capture

(2021) Harini Narayanan et al.   JOURNAL OF CHROMATOGRAPHY A

Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Capture Chromatographic Step

(2021) Harini Narayanan et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Hybrid‐EKF: Hybrid Model coupled with Extended Kalman Filter for real‐time monitoring and control of mammalian cell culture

(2020) Harini Narayanan et al.   BIOTECHNOLOGY AND BIOENGINEERING

AI Feynman: A physics-inspired method for symbolic regression

(2020) Silviu-Marian Udrescu et al.   Science Advances

Study of Lotka–Volterra Biological or Chemical Oscillator Problem Using the Normalization Technique: Prediction of Time and Concentrations

(2020) Juan Francisco Sánchez-Pérez et al.   Mathematics

Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery

(2020) Samuel Kim et al.   IEEE Transactions on Neural Networks and Learning Systems

Rigorous Derivation of the Nonlocal Reaction-Diffusion Fitzhugh--Nagumo System

(2019) Joachim Crevat et al.   SIAM JOURNAL ON MATHEMATICAL ANALYSIS

Model selection for hybrid dynamical systems via sparse regression

(2019) N. M. Mangan et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Sparse learning of partial differential equations with structured dictionary matrix

(2019) Xiuting Li et al.   CHAOS

A hybrid modeling approach integrating first principles models with subspace identification

(2019) Debanjan Ghosh et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

A new generation of predictive models: The added value of hybrid models for manufacturing processes of therapeutic proteins

(2019) Harini Narayanan et al.   BIOTECHNOLOGY AND BIOENGINEERING

Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression

(2019) Martin Schmelzer et al.   FLOW TURBULENCE AND COMBUSTION

Characterization of the equivalence of robustification and regularization in linear and matrix regression

(2018) Dimitris Bertsimas et al.   EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Sparse learning of stochastic dynamical equations

(2018) Lorenzo Boninsegna et al.   JOURNAL OF CHEMICAL PHYSICS

A global MINLP approach to symbolic regression

(2018) Alison Cozad et al.   MATHEMATICAL PROGRAMMING

Hybrid modeling as a QbD/PAT tool in process development: an industrial E. coli case study

(2016) Moritz von Stosch et al.   BIOPROCESS AND BIOSYSTEMS ENGINEERING

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

(2016) Steven L. Brunton et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Predator-prey dynamics stabilised by nonlinearity explain oscillations in dust-forming plasmas

(2016) A. E. Ross et al.   Scientific Reports

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series

(2016) Anna Klimovskaia et al.   PLoS Computational Biology

Real time optimization based on a serial hybrid model for gold cyanidation leaching process

(2015) Jun Zhang et al.   MINERALS ENGINEERING

Alfred J. Lotka and the origins of theoretical population ecology

(2015) Sharon Kingsland   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Hybrid modeling for quality by design and PAT-benefits and challenges of applications in biopharmaceutical industry

(2014) Moritz von Stosch et al.   Biotechnology Journal

Dynamic optimization of distributed biological systems using robust and efficient numerical techniques

(2012) Carlos Vilas et al.   BMC Systems Biology

Hybrid modeling framework for process analytical technology: Application to Bordetella pertussis cultures

(2011) M. von Stosch et al.   BIOTECHNOLOGY PROGRESS

Hybrid modeling for the prediction of leaching rate in leaching process based on negative correlation learning bagging ensemble algorithm

(2011) Guanghao Hu et al.   COMPUTERS & CHEMICAL ENGINEERING

A novel identification method for hybrid (N)PLS dynamical systems with application to bioprocesses

(2011) M. von Stosch et al.   EXPERT SYSTEMS WITH APPLICATIONS

Identification of semi-parametric hybrid process models

(2010) Aidong Yang et al.   COMPUTERS & CHEMICAL ENGINEERING

Model-based design of experiments for parameter precision: State of the art

(2007) Gaia Franceschini et al.   CHEMICAL ENGINEERING SCIENCE

A Hybrid Model Framework for the Optimization of Preparative Chromatographic Processes

(2004) Deepak Nagrath et al.   BIOTECHNOLOGY PROGRESS