23 papers · ranked by Valyu relevance
Lawrence C. Andrews, Herbert J. Bernstein, Nicholas K. Sauter
The unit-cell reduction described by Selling and used by Delone (Delaunay) is explained in a simple form.
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…
Anika Küken, Philipp Wendering, Damoun Langary, Zoran Nikoloski
Large-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of…
Mojtaba Tefagh, Stephen Boyd
Genome-scale metabolic networks are exceptionally huge and even efficient algorithms can take a while to run because of the sheer size of the problem instances. To address this problem, metabolic network reductions can substantially reduce the overwhelming size of the problem instances at hand. We begin by formulating…
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…
Elena Kutumova, Andrei Zinovyev, Ruslan Sharipov, Fedor Kolpakov
Background Many mathematical models characterizing mechanisms of cell fate decisions have been constructed recently. Their further study may be impossible without development of methods of model composition, which is complicated by the fact that several models describing the same processes could use different reaction…
Kyohei Hayashi, Jeremy Griffin, Kaid Harper, Yu Kawamata + 1 more
Arene semi-reduction remains a challenge when multiple re-ductively labile functional groups are present or when using heteroarene substrates. Conventional chemical and even elec-trochemical Birch-type reductions suffer from a lack of chemoselectivity due to a reliance on alkali metals or harshly reducing conditions.…
Qian Yang, Carlos A. Sing-Long, Evan J. Reed
We propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale elementary reaction networks from data generated by a single or few molecular dynamics simulations (MD).
Marina Vegué, Vincent Thibeault, Patrick Desrosiers, Antoine Allard + 1 more
'Petros Koumoutsakos'] Title: Abstract Dimension reduction is a common strategy to study nonlinear dynamical systems composed by a large number of variables. The goal is to find a smaller version of the system whose time evolution is easier to predict while preserving some of the key dynamical features of the original…
S. H. Smith, Mao Zeng
We present a new algorithm for integration-by-parts (IBP) reduction of Feynman integrals with high powers of numerators or propagators, a demanding computational step in evaluating multi-loop scattering amplitudes. The algorithm starts with solving syzygy equations in individual sectors to produce IBP operators that…
Tongxi Lin, Xiaojun Ren, Xinyue Wen, Amir Karton + 2 more
Reduced graphene oxide (rGO) is a widely studied electrode material for energy storage, however, its strong re-stacking tendency during chemical reduction always leads to a degraded specific surface area and thus limits its performance. Therefore, it is necessary to control the morphology of rGO during the reduction…
Ayush Pandey, Richard M. Murray
We present a Python-based software package to automatically obtain phenomenological models of input-controlled synthetic biological circuits that guide the design using chemical reaction-level descriptive models. From the parts and mechanism description of a synthetic biological circuit, it is easy to obtain a chemical…
Mikael Sunnåker, Gunnar Cedersund, Mats Jirstrand
Background Models of biochemical systems are typically complex, which may complicate the discovery of cardinal biochemical principles. It is therefore important to single out the parts of a model that are essential for the function of the system, so that the remaining non-essential parts can be eliminated. However…
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…
Rafael Rodríguez-Puente, Manuel S Lazo-Cortés
The use of Geographic Information Systems has increased considerably since the eighties and nineties. As one of their most demanding applications we can mention shortest paths search. Several studies about shortest path search show the feasibility of using graphs for this purpose. Dijkstra’s algorithm is one of the…
Edward J. O'Loughlin, Maxim I. Boyanov, Kenneth M. Kemner, David R. Burris
The reductive dechlorination of carbon tetrachloride (CT) was examined in aqueous suspensions of sulfate green rust (GRSO4) amended with either Co(II), Cr(VI), Hg(II), Mn(II), Mo(VI), Ni(II), Pb(II), V(III), or Zn(II). The rate of CT reduction in the Hg(II)-amended GRSO4 suspension was ~1000 times faster than in…
Opeoluwa Owoyele
This paper presents a data-driven approach, referred to as Quantized Skeletal Learning (QSL), for generating skeletal mechanisms. The approach has two key components: (1) a weight vector that can be used to eliminate relatively unimportant species and reactions, and (2) an end-to-end differentiable program whose…
Authors not listed
Reductive cross-couplings have emerged as a powerful strategy for forging C–C bonds directly from electrophiles, circumventing the need for preformed organometallic reagents, yet they often suffer from limitations associated with heterogeneous reductants like Zn (e.g., poor reproducibility and scalability) or costly…
Ori Lahav, Guy Katz
— Deep neural networks (DNNs) play an increasingly important role in various computer systems. In order to create these networks, engineers typically specify a desired topology, and then use an automated training algorithm to select the network's weights. While training algorithms have been studied extensively and are…
Antoine Wallabregue, Hannah Bolland, Stephen Faulkner, Ester Hammond + 1 more
Hypoxia (low oxygen levels) exists in a wide range of biological contexts, including plants roots, bacterial biofilms, and solid tumors. In all cases, hypoxia elicits responses affecting the biological system that is experiencing low oxygen that impact on its survival. In the case of bacterial biofilms and tumors…
Authors not listed
The recent release of Meta's Open Molecules 2025 dataset (OMol25) has enabled the creation of pretrained NNPs that can predict the energy of unseen molecules in a variety of charge and spin states. However, these models do not explicitly consider charge- or spin-based physics, potentially impacting the accuracy of…
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…
AKHIL SHAJAN, Madushanka Manathunga, Andreas Goetz, Kenneth Merz
Based on a series of energy minimizations with starting structures obtained from the Baker test set of 30 organic molecules, a comparison is made between various open-source geometry optimization codes that are interfaced with the open-source QUantum Interaction Computational Kernel (QUICK) program for gradient and…