Hybrid modeling of first-principles and machine learning: A step-by-step tutorial review for practical implementation
In recent years, the integration of mechanistic process models with advanced machine
learning techniques has led to the development of hybrid models, which have shown …
learning techniques has led to the development of hybrid models, which have shown …
Stimuli-responsive viscosity modifiers
Stimuli responsive viscosity modifiers entail an important class of materials which allow for
smart material formation utilizing various stimuli for switching such as pH, temperature, light …
smart material formation utilizing various stimuli for switching such as pH, temperature, light …
Achieving optimal paper properties: A layered multiscale kMC and LSTM-ANN-based control approach for kraft pul**
The growing demand for various types of paper highlights the importance of optimizing the
kraft pul** process to achieve desired paper properties. This work proposes a novel …
kraft pul** process to achieve desired paper properties. This work proposes a novel …
Multiobjective Optimization of Plastic Waste Sorting and Recycling Processes Considering Economic Profit and CO2 Emissions Using Nondominated Sorting Genetic …
Plastic waste has become a severe threat to the environment as increasing amounts of
plastic waste are generated every year. To solve this problem, it is crucial to increase the …
plastic waste are generated every year. To solve this problem, it is crucial to increase the …
Unveiling latent chemical mechanisms: Hybrid modeling for estimating spatiotemporally varying parameters in moving boundary problems
Hybrid modeling has gained substantial recognition due to its capacity to seamlessly
integrate machine learning methodologies while preserving the fundamental physical …
integrate machine learning methodologies while preserving the fundamental physical …
Dynamic, hollow nanotubular networks with superadjustable pH-responsive and temperature resistant rheological characteristics
Recently, the interest in stimuli-responsive and adaptable materials has continuously grown
in various fields and applications. For such responsive systems, different triggers, including …
in various fields and applications. For such responsive systems, different triggers, including …
Physics-based penalization for hyperparameter estimation in gaussian process regression
Abstract In Gaussian Process Regression (GPR), hyperparameters are often estimated by
maximizing the marginal likelihood function. However, this data-dominant hyperparameter …
maximizing the marginal likelihood function. However, this data-dominant hyperparameter …
SAXS-guided unbiased coarse-grained Monte Carlo simulation for identification of self-assembly nanostructures and dimensions
Recent studies have shown that solvated amphiphiles can form nanostructured self-
assemblies called dynamic binary complexes (DBCs) in the presence of ions. Since the …
assemblies called dynamic binary complexes (DBCs) in the presence of ions. Since the …
Nanostructural and rheological transitions of pH-responsive supramolecular systems involving a zwitterionic amphiphile and a triamine
Hypothesis Supramolecular aqueous complexes of a synthesized zwitterionic surfactant, 2-
(dimethyl (octadecyl) ammonio) acetate (stearyl betaine) and diethylenetriamine, have been …
(dimethyl (octadecyl) ammonio) acetate (stearyl betaine) and diethylenetriamine, have been …
Achieving robustness in hybrid models: A physics-informed regularization approach for spatiotemporal parameter estimation in PDEs
Recent advancements in computational modeling have led to the development of hybrid
models, combining Machine Learning's data-driven adaptability with the insights of complex …
models, combining Machine Learning's data-driven adaptability with the insights of complex …