24 papers · ranked by Valyu relevance
Agus Hasan
Title: Graphical abstract
Begoña Ispizua, Josu Doncel, Jalel Ben-Othman
Mean-field approximation is a method to investigate the behavior of stochastic models formed by a large number of interacting objects. A new approximation was recently established, i.e., the refined mean-field approximation, and its high accuracy when the number of objects is small has been shown. In this work, we…
Abid Hossain Khan, Salauddin Omar, Nadia Mushtary, Richa Verma + 2 more
'Dinesh Kumar' 'Syed Bahauddin Alam'] Surrogate modeling has brought about a revolution in computation in the branches of science and engineering. Backed by Artificial Intelligence, a surrogate model can present highly accurate results with a significant reduction in computation time than computer simulation of actual…
Jun Lu, Yudong Fang, Weijian Han, Yan Wang
Surrogate models are commonly used as a substitute for the computation-intensive simulations in design optimization. However, building a high-accuracy surrogate model with limited samples remains a challenging task. In this paper, a novel adaptive-weight ensemble surrogate modeling method is proposed to address this…
Aryan Deshwal, Cory Simon, Janardhan Rao Doppa
Given a gas storage or separation task, we wish to search a library of nanoporous materials (NPMs) for the one with the optimal adsorption property. The high cost of measuring the adsorption property of an NPM, whether in the lab or a simulation, precludes exhaustive search. We explain, demonstrate, and advocate…
Muhammad Farman, Muhammad Farhan Tabassum, Muhammad Saeed, Nazir Ahmad Chaudhry
Hepatitis B is the main public health problem of the whole world. In epidemiology, mathematical models perform a key role in understanding the dynamics of infectious diseases. This paper proposes Padé approximation (Pa) with Differential Evolution (DE) for obtaining solution of Hepatitis-B model which is nonlinear…
Zhixiao Zhu, Maria Christodoulou, David Steinsaltz
Many complex systems are modelled using modular models, where individual sub-models are estimated separately and then combined. While this simplifies inference, it fails to account for interactions between components. A natural solution is to estimate all components jointly, but this is often impractical due to…
Maurice HT Ling
Modeling and simulation are recognized as important aspects of the scientific method for more than 70 years but its adoption in biology has been slow. Debates on its representativeness, usefulness, and whether the effort spent on such endeavors is worthwhile, exist to this day. Here, I argue that most of learning is…
Alexander Churkin, Stephanie Lewkiewicz, Vladimir Reinharz, Harel Dahari + 1 more
'Harel Dahari' 'Danny Barash'] Parameter estimation in mathematical models that are based on differential equations is known to be of fundamental importance. For sophisticated models such as age-structured models that simulate biological agents, parameter estimation that addresses all cases of data points available…
Luke Allan, Tim Zuehlsdorff
The second order cumulant method offers a promising pathway to predicting optical properties in condensed phase systems. It allows for the computation of linear absorption spectra from excitation energy fluctuations sampled along molecular dynamics (MD) trajectories, fully accounting for vibronic effects, direct…
Elena Kutumova, Andreï Zinovyev, Ruslan Sharipov, Fedor Kolpakov
1 Institute of Systems Biology, Ltd, 15 Detskiy proezd, Novosibirsk 630090, Russia 2Design Technological Institute of Digital Techniques, The Siberian Branch of The Russian Academy of Sciences, 6 Acad. Rzhanov Str., Novosibirsk 630090, Russia 3 Institute Curie, 26 rue d'Ulm, Paris 75248, France 3 Institut Curie, 26 rue…
Authors not listed
Model Hamiltonians represent a convenient way of reducing complex problems of many-electron quantum mechanics to much simpler problems: they can fully reproduce the core behaviors of a system of interest by encoding only the dominant physical interactions and using only a small number of associated parameters. Model…
Giulio Caravagna, Luca Bortolussi, Guido Sanguinetti
Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study. Such different models often provide qualitatively concordant predictions over specific parametrisations, but it is generally unclear whether model predictions are quantitatively in agreement…
Chenxi Wang, Jihui Zhao, Jingjing Zheng, Barak Raveh + 2 more
Developing and optimizing models for complex systems poses challenges due to the inherent complexity introduced by multiple types of input information and sources of uncertainty. In this study, we utilize Bayesian formalism to analytically examine the propagation of probability in the modeling process and propose…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Beatrix C. Hiesmayr, Marc‐Thorsten Hütt
A recent trend in mathematical modeling is to publish the computer code together with the research findings. Here we explore the formal question, whether and in which sense a computer implementation is distinct from the mathematical model. We argue that, despite the convenience of implemented models, a set of implicit…
Fortunato Bianconi, Chiara Antonini, Lorenzo Tomassoni, Paolo Valigi
Computational modeling is a remarkable and common tool to quantitatively describe a biological process. However, most model parameters, such as kinetics parameters, initial conditions and scale factors, are usually unknown because they cannot be directly measured. Therefore, key issues in Systems Biology are model…
Marco Viceconti, Miguel A. Juárez, Cristina Curreli, Marzio Pennisi + 2 more
'Giulia Russo' 'Francesco Pappalardo'] Abstract— Different research communities have developed various approaches to assess the credibility of predictive models. Each approach usually works well for a specific type of model, and under some epistemic conditions that are normally satisfied within that specific research…
Jerry Jacob, Nitish Patel, Sucheta Sehgal
Computational models of the cell can be used to study the impact of drugs and assess pathological risks. Typically, computational models are computationally demanding or difficult to implement in dedicated hardware for real-time emulation. A new Frequency Modulation (FM) model is proposed to address these limitations.…
Klaus‐Dieter Sommer, P M Harris, Sascha Eichstädt, Roland Füßl + 9 more
'Tanja Dorst' 'Andreas Schütze' 'Michael Heizmann' 'Nadine Schiering' 'Andreas Maier' 'Yuhui Luo' 'Christos Tachtatzis' 'Ivan Andonović' 'Gordon Gourlay'] - 1 Technische Universitaet Ilmenau, Germany - 2 National Physical Laboratory, Teddington, United Kingdom - 3 Physikalisch-Technische Bundesanstalt, Braunschweig and…
Authors not listed
Hydration free energy (HFE) of molecules is a fundamental property having impor- tance throughout chemistry and biology. Calculation of the HFE can be challenging and expensive with classical molecular dynamics simulation-based approaches. Ma- chine learning (ML) models are increasingly being used to predict HFE.…
Søren Saxmose Nielsen, Julio Alvarez, Paolo Calistri, Elisabetta Canali + 18 more
'Elisabetta Canali' 'Julian Ashley Drewe' 'Bruno Garin‐Bastuji' 'José Luis Gonzales Rojas' 'Christian Gortázar' 'Mette Herskin' 'Virginie Michel' 'Miguel Ángel Miranda Chueca' 'Barbara Padalino' 'Paolo Pasquali' 'Helen Clare Roberts' 'Hans Spoolder' 'Karl Ståhl' 'Antonio Velarde' 'Arvo Viltrop' 'Christoph Winckler'…
Authors not listed
Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…
Andrey A. Toropov, Alla P. Toropova, Alessandra Roncaglioni, Emilio Benfenati + 3 more
'Emilio Benfenati' 'Danuta Leszczynska' 'Jerzy Leszczynski' 'Marzio Rosi'] Data on Henry’s law constants make it possible to systematize geochemical conditions affecting atmosphere status and consequently triggering climate changes. The constants of Henry’s law are desired for assessing the processes related to…