Zobrazeno 1 - 10
of 2 403
pro vyhledávání: '"Jirasek, A."'
Autor:
Faltus, Ondřej, Jirásek, Milan, Horák, Martin, Doškář, Martin, Peerlings, Ron, Zeman, Jan, Rokoš, Ondřej
Pattern-forming metamaterials feature microstructures specifically designed to change the material's macroscopic properties due to internal instabilities. These can be triggered either by mechanical deformation or, in the case of active materials, by
Externí odkaz:
http://arxiv.org/abs/2410.19519
Chemputation is the process of programming chemical robots to do experiments using a universal symbolic language, but the literature can be error prone and hard to read due to ambiguities. Large Language Models (LLMs) have demonstrated remarkable cap
Externí odkaz:
http://arxiv.org/abs/2410.06384
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
The proposed two-dimensional geometrically exact beam element extends our previous work by including the effects of shear distortion, and also of distributed forces and moments acting along the beam. The general flexibility-based formulation exploits
Externí odkaz:
http://arxiv.org/abs/2410.04915
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
Accelerated impressed current testing is the most common experimental method for assessing the susceptibility to corrosion-induced cracking, the most prominent challenge to the durability of reinforced concrete structures. Although it is well known t
Externí odkaz:
http://arxiv.org/abs/2409.05889
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