23 papers · ranked by Valyu relevance
Thomas J. Snowden, Piet H. van der Graaf, Marcus J. Tindall
Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed…
Zhimian Hao, Magda Barecka, Alexei Lapkin
Net zero requires an accelerated transition from fossil fuels to renewables. Carbon capture and utilization (CCU) can be an effective intermediate solution for the decarbonization of fossil fuels. However, many research works contain renewables in the design of CCU systems, which may mislead stakeholders regarding the…
Rami Béchara, Hamzeh Hamadeh, Olivier Mirgaux, Fabrice Patisson
Iron ore direct reduction is an attractive alternative steelmaking process in the context of greenhouse gas mitigation. To simulate the process and explore possible optimization, we developed a systemic, multiscale process model. The reduction of the iron ore pellets is described using a specific grain model…
Guohua Wu, Witold Pedrycz, Haifeng Li, Dishan Qiu + 2 more
'Jin Liu'] Discovering and utilizing problem domain knowledge is a promising direction towards improving the efficiency of evolutionary algorithms (EAs) when solving optimization problems. We propose a knowledge-based variable reduction strategy (VRS) that can be integrated into EAs to solve unconstrained and…
Omid Mokhtari, Samuel Chevalier, Mads Almassalkhi
—Network reduction simplifies complex electrical networks to address computational challenges of large-scale transmission and distribution grids. Traditional network reduction methods are often based on a predefined set of nodes or lines to remain in the reduced network. This paper builds upon previous work on Optimal…
Na Zhang, Dongmei Wang, Kai Li, Kaiyang Wei + 3 more
'Manzhi Yang' 'Reza Teimouri'] Achieving high-precision positioning operations in a small space was of great significance in aerospace, biomedical, and other fields. In order to obtain smaller displacements with higher accuracy, this paper focused on the design, optimization, and performance analysis of a two-stage…
Mochamad Apri, Maarten de Gee, Simon van Mourik, Jaap Molenaar + 1 more
'Marie-Joelle Virolle'] Biochemical systems involving a high number of components with intricate interactions often lead to complex models containing a large number of parameters. Although a large model could describe in detail the mechanisms that underlie the system, its very large size may hinder us in understanding…
Boon Xian Chai, Boris Eisenbart, Mostafa Nikzad, Bronwyn Fox + 12 more
'Yuqi Wang' 'Kyaw Hlaing Bwar' 'Kaiyu Zhang' 'Marcin Sosnowski' 'Jaroslaw Krzywanski' 'Karolina Grabowska' 'Dorian Skrobek' 'Ghulam Moeen Uddin' 'Yunfei Gao' 'Anna Zylka' 'Anna Kulakowska' 'Bachil El Fil'] The utilisation of numerical process simulation has greatly facilitated the challenging task of liquid composite…
Shannon Bonke, Giovanni Trezza, Luca Bergamasco, Hongwei Song + 4 more
The sunlight-driven reduction of CO2 into fuels and platform chemicals is a promising approach to enable a circular economy. However, established optimisation approaches are poorly suited to multi-variable multi-metric photocatalytic systems because they aim to optimise one performance metric while sacrificing the…
Jincheng Mei, Hao Zhang, Bao‐Liang Lu
The scalability of submodular optimization methods is critical for their usability in practice. In this paper, we study the reducibility of submodular functions, a property that enables us to reduce the solution space of submodular optimization problems without performance loss. We introduce the concept of reducibility…
Jordan T. Sturdy, Anne K. Silverman, Nathan T. Pickle
The residual reduction algorithm (RRA) in OpenSim improves dynamic consistency of movement simulations of musculoskeletal models. RRA requires the user to select numerous tracking weights for the joint kinematics to reduce residual errors. Selection is often performed manually, which can be time-consuming and is…
Tianyi Chen, Zhi‐Qin John Xu
Neural networks have been extensively applied to a variety of tasks, achieving astounding results. Applying neural networks in the scientific field is an important research direction that is gaining increasing attention. In scientific applications, the scale of neural networks is generally moderate size, mainly to…
Chattriya Jariyavajee, Suthida Fairee, Charoenchai Khompatraporn, Jumpol Polvichai + 2 more
'Jumpol Polvichai' 'Booncharoen Sirinaovakul' 'Sakdirat Kaewunruen'] This study addresses a cutting stock problem in steel cutting industry by developing a mathematical model in which machine specifications and cutting conditions are constraints. The solution process involves three key steps: (i) Problem…
Ayush Pandey, Richard M. Murray
We present an automated model reduction algorithm that uses quasi-steady state approximation based reduction to minimize the error between the desired outputs. Additionally, the algorithm minimizes the sensitivity of the error with respect to parameters to ensure robust performance of the reduced model in the presence…
K. Eswaran
T HE problem of optimizing a linear functional subject to a set of linear constraints (the so called Linear Programming or LP problem) has attracted many researchers; the first fundamental contributions to the LP problem was done by Kantorovich [1] and Dantzig [2], who first discovered the Simplex method, which is…
Ignacio Tapia García, Cristóbal Torrealba, Ricardo Luna, José Ricardo Pérez-Correa + 1 more
Dynamic Flux Balance Analysis (DFBA) enables simulation of microbial culture dynamics under changing environmental conditions, but remains computationally expensive for tasks such as parameter calibration and fermentation optimization when applied using genome-scale metabolic models (GEMs). To address this challenge…
Riley Hickman, Priyansh Parakh, Austin Cheng, Qianxiang Ai + 3 more
Experiment planning algorithms are a required component of autonomous platforms for scientific discovery. Selecting a suitable optimization algorithm for a novel application is an important yet difficult choice a researcher has to make based on past empirical performance on similar tasks. To facilitate the evaluation…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Chengcheng Li, Zi Wang, Dali Wang, Xiangyang Wang + 1 more
Most existing channel pruning methods formulate the pruning task from a perspective of inefficiency reduction which iteratively rank and remove the least important filters, or find the set of filters that minimizes some reconstruction errors after pruning. In this work, we investigate the channel pruning from a new…
Alejandro F. Villaverde, Fabian Fröhlich, Daniel Weindl, Jan Hasenauer + 1 more
Mechanistic kinetic models usually contain unknown parameters, which need to be estimated by optimizing the fit of the model to experimental data. This task can be computationally challenging due to the presence of local optima and ill-conditioning. While a variety of optimization methods have been suggested to…
Authors not listed
With the ever-increasing demand for atomistic structures representative of real-life systems as well as the ad-vent of exascale computers, it has now become necessary and possible to use advanced global optimization (GO) techniques to intelligently sample the potential energy surface (PES). Given the previous studies…
Fabian Fröhlich, Peter K. Sorger
Ordinary differential equation (ODE) models are widely used to describe biochemical processes, since they effectively represent mass action kinetics. Optimization-based calibration of ODE models on experimental data can be challenging, even for low-dimensional problems. However, reliable model calibration is a…
Fabian Fröhlich, Barbara Kaltenbacher, Fabian J. Theis, Jan Hasenauer
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small-and medium-scale biological processes. While the same should in principle hold for large-and genome-scale processes, the computational methods for the analysis of…