Zobrazeno 1 - 10
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pro vyhledávání: '"Jirasek, Fabian"'
Predicting the thermodynamic properties of mixtures is crucial for process design and optimization in chemical engineering. Machine learning (ML) methods are gaining increasing attention in this field, but experimental data for training are often sca
Externí odkaz:
http://arxiv.org/abs/2410.06060
Autor:
Nagda, Mayank, Ostheimer, Phil, Specht, Thomas, Rhein, Frank, Jirasek, Fabian, Kloft, Marius, Fellenz, Sophie
Physics-Informed Neural Networks (PINNs) have emerged as a promising method for approximating solutions to partial differential equations (PDEs) using deep learning. However, PINNs, based on multilayer perceptrons (MLP), often employ point-wise predi
Externí odkaz:
http://arxiv.org/abs/2409.20206
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
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
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
Autor:
Jirasek, Fabian, Bamler, Robert, Fellenz, Sophie, Bortz, Michael, Kloft, Marius, Mandt, Stephan, Hasse, Hans
Publikováno v:
Chemical Science 13 (2022) 4854-4862
Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (continuous) conditions like temperature and concentration. On the other
Externí odkaz:
http://arxiv.org/abs/2209.00605
Publikováno v:
Chemical Communications 56 12407, 2020
We present a generic way to hybridize physical and data-driven methods for predicting physicochemical properties. The approach `distills' the physical method's predictions into a prior model and combines it with sparse experimental data using Bayesia
Externí odkaz:
http://arxiv.org/abs/2202.08804
Embeddings of high-dimensional data are widely used to explore data, to verify analysis results, and to communicate information. Their explanation, in particular with respect to the input attributes, is often difficult. With linear projects like PCA
Externí odkaz:
http://arxiv.org/abs/2108.08706