Zobrazeno 1 - 10
of 2 384
pro vyhledávání: '"Jirasek, A"'
Data depth has emerged as an invaluable nonparametric measure for the ranking of multivariate samples. The main contribution of depth-based two-sample comparisons is the introduction of the Q statistic (Liu and Singh, 1993), a quality index. Unlike t
Externí odkaz:
http://arxiv.org/abs/2408.11003
Autor:
Kahana, Amit, MacLeod, Alasdair, Mehr, Hessam, Sharma, Abhishek, Carrick, Emma, Jirasek, Michael, Walker, Sara, Cronin, Leroy
Here we demonstrate the first biochemistry-agnostic approach to map evolutionary relationships at the molecular scale, allowing the construction of phylogenetic models using mass spectrometry (MS) and Assembly Theory (AT) without elucidating molecula
Externí odkaz:
http://arxiv.org/abs/2408.09305
Accurate prediction of thermodynamic properties is pivotal in chemical engineering for optimizing process efficiency and sustainability. Physical group-contribution (GC) methods are widely employed for this purpose but suffer from historically grown,
Externí odkaz:
http://arxiv.org/abs/2408.05220
We present the first hard-constraint neural network for predicting activity coefficients (HANNA), a thermodynamic mixture property that is the basis for many applications in science and engineering. Unlike traditional neural networks, which ignore ph
Externí odkaz:
http://arxiv.org/abs/2407.18011
We demonstrate that thermodynamic knowledge acquired by humans can be transferred to computers so that the machine can use it to solve thermodynamic problems and produce explainable solutions with a guarantee of correctness. The actionable knowledge
Externí odkaz:
http://arxiv.org/abs/2407.17169
The paper develops a new integral micromorphic elastic continuum model, which can describe dispersion properties of band-gap metamaterials, i.e., metamaterials that inhibit propagation of waves in a certain frequency range. The enrichment consists in
Externí odkaz:
http://arxiv.org/abs/2407.10676
Predicting the physico-chemical properties of pure substances and mixtures is a central task in thermodynamics. Established prediction methods range from fully physics-based ab-initio calculations, which are only feasible for very simple systems, ove
Externí odkaz:
http://arxiv.org/abs/2406.08075
A model for corrosion-induced cracking of reinforced concrete subjected to non-uniform chloride-induced corrosion is presented. The gradual corrosion initiation of the steel surface is investigated by simulating chloride transport considering binding
Externí odkaz:
http://arxiv.org/abs/2312.06209
We present a new mechanistic framework for corrosion-induced cracking in reinforced concrete that resolves the underlying chemo-mechanical processes. The framework combines, for the first time, (i) a model for reactive transport and precipitation of
Externí odkaz:
http://arxiv.org/abs/2306.01903
Autor:
Hartung, Fabian, Franks, Billy Joe, Michels, Tobias, Wagner, Dennis, Liznerski, Philipp, Reithermann, Steffen, Fellenz, Sophie, Jirasek, Fabian, Rudolph, Maja, Neider, Daniel, Leitte, Heike, Song, Chen, Kloepper, Benjamin, Mandt, Stephan, Bortz, Michael, Burger, Jakob, Hasse, Hans, Kloft, Marius
This paper provides the first comprehensive evaluation and analysis of modern (deep-learning) unsupervised anomaly detection methods for chemical process data. We focus on the Tennessee Eastman process dataset, which has been a standard litmus test t
Externí odkaz:
http://arxiv.org/abs/2303.05904