147 p.While probabilistic modelling has been widely used in the last decades, the quantitative prediction in stochastic modelling of real physical problems remains a great challenge and requires sophisticated mathematical models and advanced numerical algoritms. In this study, we developed the mathematical tools for the quantitative prediction of three applications in Polymer Science and Quantum Measurements theory. In particular, we addressed a stochastic approach for the quantitative modelling of Controlled Radical Polymerization. Then, a Population Balance Equations based framework was derived for the on-the-fly prediction of Multi-phase Polymers Morphology. Finally, we designed a stochastic simulation framework for measurements performe...
The generalized Langevin equation (GLE) has been used to describe the dynamics of particles in a sta...
Kinetic Monte Carlo modeling is applied for the coupled simulation of the chain length and particle ...
This reprint is a compilation of nine papers published in Processes, in a Special Issue on “Modeling...
The stochastic simulation algorithm (SSA) and the corresponding Monte Carlo (MC) method are among th...
The objective of this thesis is to propose modeling techniques that enable the design and optimizati...
The random sampling technique is a powerful Markovian method that can be applied to any types of non...
Polymer chain microstructure, including characteristics such as molecular weight and branch length, ...
Two different approaches to parameter estimation (PE) in the context of polymerization are introduce...
Recent developments of a method based upon population balances of generating functions of polymer ch...
Throughout the last 25 years, computational chemistry based on quantum mechanics has been applied to...
Using simple exactly solvable models, we show that event-dependent time delays may lead to significa...
The geometric copolymerization model is a recently introduced statistical Markov chain model. Here, ...
A mathematical model of RAFT polymerization processes is presented capable of predicting the full mo...
Sampling equilibrium ensembles of dense polymer mixtures is a paradigmatically hard problem in compu...
Predicting chain microstructure became an important task for polymer scientists. Polydisperse nature...
The generalized Langevin equation (GLE) has been used to describe the dynamics of particles in a sta...
Kinetic Monte Carlo modeling is applied for the coupled simulation of the chain length and particle ...
This reprint is a compilation of nine papers published in Processes, in a Special Issue on “Modeling...
The stochastic simulation algorithm (SSA) and the corresponding Monte Carlo (MC) method are among th...
The objective of this thesis is to propose modeling techniques that enable the design and optimizati...
The random sampling technique is a powerful Markovian method that can be applied to any types of non...
Polymer chain microstructure, including characteristics such as molecular weight and branch length, ...
Two different approaches to parameter estimation (PE) in the context of polymerization are introduce...
Recent developments of a method based upon population balances of generating functions of polymer ch...
Throughout the last 25 years, computational chemistry based on quantum mechanics has been applied to...
Using simple exactly solvable models, we show that event-dependent time delays may lead to significa...
The geometric copolymerization model is a recently introduced statistical Markov chain model. Here, ...
A mathematical model of RAFT polymerization processes is presented capable of predicting the full mo...
Sampling equilibrium ensembles of dense polymer mixtures is a paradigmatically hard problem in compu...
Predicting chain microstructure became an important task for polymer scientists. Polydisperse nature...
The generalized Langevin equation (GLE) has been used to describe the dynamics of particles in a sta...
Kinetic Monte Carlo modeling is applied for the coupled simulation of the chain length and particle ...
This reprint is a compilation of nine papers published in Processes, in a Special Issue on “Modeling...