Dear Colleagues,

Over the last decades, various computational, statistical, or machine-learning based models have been developed for research in life sciences as well as for process engineering. Standard approaches include, but are not limited to, mechanistic models, hybrid mechanistic-machine learning models, statistical approaches, computational fluid dynamics and lattice-Boltzmann methods, molecular models, and ab-initio and atomistic/coarse-grained molecular dynamic simulations. Besides academic research, nowadays, industrial manufacturing, development, and research also explore the potential of these methods for the development of processes and novel products like active pharmaceutical ingredients. Despite the ongoing success, crucial and non-trivial challenges are the calibration and the validation procedure of the chosen model. Hence, it is often not obvious how to calibrate the model parameters with regard to the experimental data. Even more importantly, reliable validation criteria to verify the outcomes of the calculations are often missing. Such standards are of the utmost importance in order to support process development and product characterization in industry, as well as scientific validation in academic research. This Special Issue aims to establish a collection of articles on reliable model validation and calibration procedures in order to define novel standards. We welcome research articles on novel methods, but also review papers, in order to give an overview about the current state-of-the-art. Topics include, but are not limited to, the following:

• Novel and scientifically sound calibration and validation procedures for computational, statistical, and machine-learning based models in life sciences and process engineering;
• Novel statistical approaches for experimental data;
• Criteria to define the validity of model outcomes, specifically, machine learning-based methods;
• Calibration protocols for coarse-grained and molecular models;
• Reliable calibration and validation procedures for mechanistic, hybrid, statistical, computational fluid dynamics, and lattice-Boltzmann simulations, as well as neural network approaches for the study of unit operation behavior in bioprocesses;
• Validation and calibration protocols for integrated processed and coupled unit operation models.

Dr. Jens Smiatek
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Model validation
• Model calibration
• Mechanistic and hybrid models
• Statistical methods and approaches
• Validation of machine-learning based methods
• Process models
• Biopharmaceutical engineering
• Life sciences
• Molecular and homology models