Zobrazeno 1 - 10
of 176
pro vyhledávání: '"Schweidtmann, Artur M"'
Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation
Autor:
Zhao, Yidong, Tourais, Joao, Pierce, Iain, Nitsche, Christian, Treibel, Thomas A., Weingärtner, Sebastian, Schweidtmann, Artur M., Tao, Qian
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
Deep learning (DL)-based methods have achieved state-of-the-art performance for many medical image segmentation tasks. Nevertheless, recent studies show that deep neural networks (DNNs) can be miscalibrated and overconfident, leading to "silent failu
Externí odkaz:
http://arxiv.org/abs/2403.02311
Autor:
Tkáč, Michal, Sieber, Jakub, Kuhlmann, Lara, Brueggenolte, Matthias, Rinciog, Alexandru, Henke, Michael, Schweidtmann, Artur M., Gao, Qinghe, Theisen, Maximilian F., Shawi, Radwa El
Machine Learning (ML) techniques are encountered nowadays across disciplines, from social sciences, through natural sciences to engineering. The broad application of ML and the accelerated pace of its evolution lead to an increasing need for dedicate
Externí odkaz:
http://arxiv.org/abs/2401.16291
The process engineering domain widely uses Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (P&IDs) to represent process flows and equipment configurations. However, the P&IDs and PFDs, hereafter called flowsheets, can contain er
Externí odkaz:
http://arxiv.org/abs/2312.02873
ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution of machine learning certification problems. However, previous works are
Externí odkaz:
http://arxiv.org/abs/2312.01228
Autor:
Balhorn, Lukas Schulze, Weber, Jana M., Buijsman, Stefan, Hildebrandt, Julian R., Ziefle, Martina, Schweidtmann, Artur M.
ChatGPT is a powerful language model from OpenAI that is arguably able to comprehend and generate text. ChatGPT is expected to have a large impact on society, research, and education. An essential step to understand ChatGPT's expected impact is to st
Externí odkaz:
http://arxiv.org/abs/2309.10048
Autor:
Kaven, Luise F., Schweidtmann, Artur M., Keil, Jan, Israel, Jana, Wolter, Nadja, Mitsos, Alexander
Microgels are cross-linked, colloidal polymer networks with great potential for stimuli-response release in drug-delivery applications, as their size in the nanometer range allows them to pass human cell boundaries. For applications with specified re
Externí odkaz:
http://arxiv.org/abs/2308.16724
Autor:
Gao, Qinghe, Schweidtmann, Artur M.
The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches. Recently, breakthroughs in artificial intelligence offer opportunities to accelerate this transition. Specifi
Externí odkaz:
http://arxiv.org/abs/2308.07822
Publikováno v:
Computer Aided Chemical Engineering Volume 52, 2023, Pages 2011-2016
Artificial intelligence has great potential for accelerating the design and engineering of chemical processes. Recently, we have shown that transformer-based language models can learn to auto-complete chemical process flowsheets using the SFILES 2.0
Externí odkaz:
http://arxiv.org/abs/2302.03379
Process design is a creative task that is currently performed manually by engineers. Artificial intelligence provides new potential to facilitate process design. Specifically, reinforcement learning (RL) has shown some success in automating process d
Externí odkaz:
http://arxiv.org/abs/2302.03375
Publikováno v:
AIChE Journal, Volume70, Issue1 January 2024 e18259
Developing Piping and Instrumentation Diagrams (P&IDs) is a crucial step during the development of chemical processes. Currently, this is a tedious, manual, and time-consuming task. We propose a novel, completely data-driven method for the prediction
Externí odkaz:
http://arxiv.org/abs/2211.05583