22 papers · ranked by Valyu relevance
Jan S. Hesthaven, Benjamin Peherstorfer, Benjamin Unger
This article surveys nonlinear model reduction methods that remain effective in regimes where linear reduced-space approximations are intrinsically inefficient, such as transport-dominated problems with wave-like phenomena and moving coherent structures, which are commonly associated with the Kolmogorov barrier. The…
Huang Yue, Sapkota, Dixant B., Singh + 1 more
—Power systems are globally experiencing an unprecedented growth in size and complexity due to the advent of nonconventional generation and consumption technologies. To navigate computational complexity, power system dynamic models are often reduced using techniques based on singular perturbation. However, several…
Guangpu Wu, Shibei Xue, Guofeng Zhang, Rebing Wu + 2 more
An augmented system model provides an effective way to model non-Markovian quantum systems, which is useful in filtering and control for this class of systems. However, since a large number of ancillary quantum oscillators representing internal modes of a non-Markovian environment directly interact with the principal…
San Dinh, Claudemi A. Nascimento, David S. Mebane, Fernando V. Lima
Implementation of Dynamic Discrepancy Reduced-Order Modeling in Advanced Process Control Authors: San Dinh, Claudemi A. Nascimento, David S. Mebane, Fernando V. Lima This paper introduces a novel framework for implementing dynamic discrepancy reduced-order modeling in advanced process control. This framework balances…
Michael Pan, Peter J. Gawthrop, Joseph Cursons, Edmund J. Crampin
Mathematical models of enzyme cycles form the basis of quantifying key features of metabolism and membrane transport. These models are often integrated into more comprehensive models such as whole-cell models to understand emergent behaviours between interacting components. However, it is currently computationally…
Yuganthi R. Liyanage, Omar Saucedo, Necibe Tuncer, Gerardo Chowell
Structural identifiability is the theoretical ability to uniquely recover model parameters from ideal, noise-free data and is a prerequisite for reliable parameter estimation in epidemic modeling. Despite its relevance in model calibration and inference, structural identifiability analysis remains underused and…
Marek Ciklamini, Matous Cejnek
This research compares Graph Neural Networks reduction techniques for computational modeling in structural engineering. The proposed Finite Element Shape Logic Graph technology serves as a benchmark, capturing intricate details through fully connected logic graphs derived from Finite Element Models. Multiple graph…
Ismaila Muhammed, Dimitris M. Manias, Dimitris A. Goussis, Haralampos Hatzikirou + 1 more
Biological systems inherently exhibit multi-scale dynamics, making accurate system identification particularly challenging due to the complexity of capturing a wide time scale spectrum. Traditional methods capable of addressing this issue rely on explicit equations, limiting their applicability in cases where only…
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…
Mohamed Tahar Mabrouk, Padmanabhan, Shri Balaji, Bruno Lacarrière + 6 more
This paper surveys the primary computational hurdles of Energy Systems optimization coming from different sources: model-induced complexity, optimization algorithm requirements, and uncertainties handling (both aleatoric and epistemic). Techniques to reduce complexity such as time-series and spatial aggregation, model…
Shengyang Wu, Vladimir Dvorkin
Dynamic models of power systems are critical for analyzing grid response to disturbances and blackouts, but the release of real-world dynamic models is hindered by privacy and cybersecurity concerns, as such models carry sensitive information about transmission, generation, and load parameters. We develop an algorithm…
Ziyue Liu, Mohammad Ahmadi Gharehtoragh, Brenna Kari Losch, David Johnson
Coastal communities can be exposed to risk from catastrophic storm-induced coastal floods, which bring huge losses to global human society each year. Substantial efforts have been devoted to storm-induced coastal hazard assessment and management. In recent decades, the rapid advancement of computational power has…
Xingzhi Liu, Haolan Huang, Yingmei Li, Zida Xia + 2 more
In the analysis of complex engineering systems, managing uncertainty and optimizing information processing structures are critical for reliable state prediction. The Belief Rule Base (BRB) provides a powerful machine learning approach for integrating expert knowledge with uncertain information. However, mitigating the…
Luis Balderas, Miguel Lastra, José M. Benítez
Large language models are transforming all areas of academia and industry, attracting the attention of researchers, professionals, and the general public. In the trek for more powerful architectures, Mixture-of-Experts, inspired by ensemble models, have emerged as one of the most effective ways to follow. However, this…
Andrea Zanoni, Gianluca Geraci, Matteo Salvador, Alison L. Marsden + 1 more
We present a new approach for nonlinear dimensionality reduction, specifically designed for computationally expensive mathematical models. We leverage autoencoders to discover a one-dimensional neural active manifold (NeurAM) capturing the model output variability, through the aid of a simultaneously learnt surrogate…
Theodore de Pomereu, Fabian Fröhlich
Cells respond to their environment through protein networks often dysregulated in cancer, making dynamical modelling crucial. Limitations in experimental data and computational resources motivate coarse-graining methods to build low-dimensional descriptions. Yet classical approaches to coarse-grained modelling rely on…
Mira Chaplin, Lars Andersland, Delaney Snead, Brian M. Pecson + 7 more
Coagulation, flocculation, and sedimentation (CFS) is widely applied as a combined unit process in the treatment of drinking water, wastewater, and recycled water; however, virus reduction through CFS has not been sufficiently characterized to assign pathogen log reduction value (LRV) credits. This study collected data…
Niklas Neubrand, Timo Rachel, Tim Litwin, Jens Timmer + 2 more
Systems biology strives to unravel the complex dynamics of cellular processes, often with the help of ordinary differential equations (ODEs). However, the sparsity of measured data and the strong non-linearity of common ODEs introduce severe numerical problems in typical modeling tasks. This gave rise to the…
Authors not listed
Machine learning interatomic potentials (MLIPs), also known as machine learning force fields (MLFFs), offer scalable means of simulating complex systems and processes at \textit{ab initio} level accuracy. One such process is the critical yet still poorly understood formation of the solid electrolyte interphase (SEI) at…
Authors not listed
Meta-GGA density functional theory (DFT) is an important method in ab initio materials modelling; however, its computational cost limits applicability for generating large datasets or simulating extended length and time scales, as necessary for modern materials discovery. Deorbitalization is a promising strategy to…
Authors not listed
Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the…
Authors not listed
In the United States, people of color are disproportionately and unjustly exposed to air pollution. Historically, environmental policy has emphasized aggregate emission reductions; yet major emission reduction scenarios do not sufficiently mitigate relative exposure disparities. Here, we show that without focusing on…